Pugnaire F.I. Valladares F. Functional Plant Ecology
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Water Relations and Hydraulic Architecture 211
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7
Responses of Plants
to Heterogeneous Light
Environments
Robert W. Pearcy
CONTENTS
Introduction ....................................................................................................................... 213
Heterogeneity and Plasticity: Spatial and Temporal Aspects............................................. 214
Spatial Heterogeneity in Light Environments .................................................................... 216
Causes and Scales of Spatial Heterogeneity ................................................................... 216
Responses to Spatial Heterogeneity................................................................................ 220
Photosynthesis and Growth ........................................................................................ 220
Plasticity of the Photosynthetic Apparatus in Response to Spatial Heterogeneity..... 220
Plasticity of Whole-Plant Responses .............................................................................. 228
Responses to within-Crown Spatial Heterogeneity......................................................... 228
Responses to Directional Light Gradients...................................................................... 230
Differential Growth of Clonal Plants ............................................................................. 231
Temporal Heterogeneity in Light Environments................................................................ 232
Timescales of Temporal Changes and Responses........................................................... 232
Responses to Sunflecks................................................................................................... 233
Temporal Nature of Sunfleck Light Regimes .............................................................233
Dynamic Responses of Photosynthesis to Sunflecks...................................................234
Models for Assessing the Consequences of Sunfleck Light Regimes.......................... 239
Growth Responses to Sunflecks..................................................................................241
Responses to Gap Dynamics ..........................................................................................242
Temporal Nature of Gap Light Regimes.................................................................... 242
Dynamic Plasticity of Leaves and Plants to Long-Term Temporal Heterogeneity..... 243
Concluding Remarks..........................................................................................................244
References .......................................................................................................................... 246
INTRODUCTION
Of all the environmental factors affecting plants, light is perhaps the most spatially and
temporally heterogeneous. This heterogeneity takes on special importance in tropical forests
in particular because here light is considered to be the single-most limiting resource for plant
growth and reproduction. Accordingly, the life cycle and physiological responses of many tree
and understory species have been shown to be closely keyed to changes in light availability
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213
(Bazzaz and Pickett 1980, Denslow 1980, 1987, Strauss-Debenedetti and Bazzaz 1991,
Canham et al. 1994, Sipe and Bazzaz 1994, Gratzer et al. 2004). Although less studied,
herbaceous and shrub communities also exhibit heterogeneity in light environments (Tang
et al. 1989, Washitani and Tang 1991, Ryel et al. 1994, 1996, Derner and Wu 2001). Much of
the emphasis in ecology has been on the community and ecosystem consequences of envir-
onmental heterogeneity. For light in particular, the role of gaps in forest regeneration and
diversity has received much attention. Several current models of forest dynamics have as
their foundation differential species responses to heterogeneous light (Friend et al. 1993,
Acevedo et al. 1996, Pacala et al. 1996, Shugart 1996, Bolker et al. 2003). Considerable
attention has also been given to the responses of individuals in terms of their ability to
capture and use light as a spatially and temporally heterogeneous resource (Caldwell et al.
1986, Chazdon 1992, Newell et al. 1993, Pearcy et al. 1994). At the level of the individual,
the concepts of heterogeneity and plasticity are intertwined. Phenotypic plasticity has a
central explanatory role in how individuals across populations differ in form and function
in response to spatial heterogeneity. At the same time, physiological constraints on organ-
ism responses to temporal heterogeneity determine to a large extent the nature of the adaptive
responses to it.
Although the concept of heterogeneity and its role in ecology has been widely discussed,
its use has been somewhat imprecise (Stuefer 1996). A heterogeneous habitat is one where a
resource is patchily distributed. This patchy distribution can be either spatial or temporal in
nature and has a certain grain size. If the grain size is small or short relative to the scale of the
functional response then the environment may be perceived as uniform. If, on the other hand,
the grain size is large or long relative to the functional response then the environment may be
perceived as nonuniform. Temporal gain size, for example, is important in determining
whether there is a selective advantage to some plastic response to the heterogeneity. Since
photosynthetic acclimation occurs over days, mechanisms that integrate and respond to daily
totals rather than instantaneous photon fluxes would be favored (Chabot 1979). On the other
hand, stomatal responses occur over minutes requiring sensing of the temporal heterogeneity
on a much shorter timescale (Zeiger et al. 1985, Tinoco-Ojanguren and Pearcy 1993a).
Patches also have contrast representing the sharpness of the transition from one patch to
the other. Finally, predictability is an important but little-considered aspect of heterogeneity
(Stuefer 1996). It is the accuracy with which a given resource availability can be predicted in
space or time. The occurrence of one sunfleck is a reasonable predictor that others follow.
Thus, a response to the first sunfleck, even if it is too delayed to significant carbon gain in that
sunfleck, can improve the usage of subsequent sunflecks (Pearcy et al. 1994).
HETEROGENEITY AND PLASTICITY: SPATIAL AND TEMPORAL ASPECTS
Although it is often convenient to separate spatial and temporal heterogeneity as I have done
in this chapter, the separation is rather artificial. Sunflecks cause the smallest scale of spatial
heterogeneity and also the most rapid scale of temporal heterogeneity. Moving up the scales
through gaps the spatial and temporal scales are highly correlated. Indeed in terms of
analysis, spatial and temporal scales are frequently interchangeable. Wavelet analysis or
autocorrelation, two commonly used techniques for analysis of heterogeneity (Baldocchi
and Collineau 1994, Saunders et al. 2005), can be applied equally well to either spatial or
temporal scales.
Differences do become apparent, however, when the nature of the responses is considered.
For temporal heterogeneity, the dynamics of the system as determined by the time constants
of the underlying processes must be considered. A dynamic system is one in which the current
rate is dependent in some way not only on current conditions but also on the rate at some
previous time. A system is at steady state when it depends only on the current input
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214 Functional Plant Ecology
parameters. Studies of spatial heterogeneity almost invariably treat systems as steady state
even though the spatial pattern observed may be a consequence of an underlying dynamics.
Of course, essentially all physiological and ecological systems are dynamic if observed on the
appropriate timescale. For example, characterizing photosynthetic responses to gap forma-
tion with a sequence of steady-state light curve measurements may be perfectly appropriate,
whereas the growth response to a gap formation needs to consider the dynamics because of
leaf turnover. Therefore, whether it is necessary to consider the dynamics depends on whether
the system is adequately described by a series of steady states occurring in response to the
changing temporal heterogeneity. If its dynamic properties are important, they cause a
significant deviation from the steady-state predictions.
The concept in spatial heterogeneity most similar to dynamics in temporal heterogeneity is
grain size (Levins 1968). Spatial heterogeneity is considered to be fine grained if the patch size
is such that the individual plant may be influenced by more than one patch. It is coarse
grained if the plant is wholly within one patch and not influenced significantly by adjacent
patches. The gap–understory dichotomy is an example of a coarse-grained patchiness,
whereas the understory itself with sunflecks and shade is an example of fine-grained patchi-
ness. Plants within a coarse-grained patch generate a fine-grained heterogeneity within their
canopy because of self-shading. Of course, grain size applies equally well to temporal
heterogeneity, and indeed, as illustrated by the sunfleck example, spatial grain size often
has a temporal origin. Therefore, the comparison of the effects of grain size to dynamics rests
on the question of whether nearby patches influence the response to the environment in each
other. Although this question has not been explored extensively it seems clear that hormonal
and hydraulic signals provide a mechanism for such interactions.
It is also necessary to consider how heterogeneity is measured and how the plant perceives
and responds to it. Measurements of the environment almost always involve linear scales,
whereas the plant may respond in a distinctly nonlinear fashion. Nonlinear functions require
sufficient temporal or spatial sampling to avoid sampling errors (Ruel and Ayres 1999). The
response of photosynthesis to PFD is a particularly relevant example since it strongly increases
as PFD increases at low values but then exhibits saturation at higher PFDs. Depending on the
range of PFDs encountered, the apparent heterogeneity in photosynthetic rates may be greater
or lesser than the apparent heterogeneity in the PFD. Since photoinhibition occurs in response
to excess PFD, the apparent heterogeneity in it would be still different. Therefore, appropriate
measures of heterogeneity, preferably based on the biological responses are important.
At the level of the individual, there can be several types of responses to environmental
heterogeneity, depending on the scale. If the supply of a limiting resource is changed its
acquisition is changed simply because of changes in diffusion rates and the kinetics of the
enzymes involved in uptake. There can also be regulatory responses that may further increase
or decrease the capacity for resource acquisition. An example would be a change in stomatal
conductance (g
s
) influencing the diffusion of CO
2
into the leaf and hence changing the CO
2
assimilation rate. Finally, there can be some change in the properties of the organism itself
such as an increase in the concentration of enzymes in the leaf that influences the capacity for
CO
2
assimilation. The latter falls clearly within the realm of a plastic response to the
environment and if it improves the performance of the plant in heterogeneous environments
then it would be considered an acclimation response. A change in g
s
would not fall within a
modern concept of plasticity. The modern concept includes those changes in an organism
that enhance performance in a given environment rather than, as originally envisioned by
Bradshaw (1965), as all the phenotypic changes observed when the environment is changed.
Thus, a phenotypic change due to some stress injury would not be included in the modern
concept of plasticity, but some adjustment that minimizes the stress injury would be included.
Regulatory responses tend to function to fine-tune the capacity of component steps, such as g
s
and the biochemical capacity for CO
2
fixation so that they are in balance with no one step
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Responses of Plants to Heterogeneous Light Environments 215
greatly in excess or limitation. The regulated variations in a character such as g
s
in response to
changing environments are not an expression of plasticity, but differences in the maximum g
s
are rightfully expressions of phenotypic plasticity.
The difference between regulatory responses and acclimation has its greatest consequence
for the time constants of the response to environmental change. A change in concentrations of
enzymes takes time for synthesis or degradation, whereas regulation of the activity of already
present enzymes can occur on a much faster timescale. Moreover, much of the plasticity
expressed in response to changing light environments involves development of new leaves that
have sun and shade characteristics, requiring even more time. Acclimation is too slow to
respond to or provide any significant advantage in sunflecks but is the dominant mechanism
for phenotypic adaptation to gap formation and subsequent canopy closure.
The evolutionary and ecological role of plasticity has received increasing attention over
the past decade or so (Schlichting 1986, Callahan et al. 1997, Sultan 2001, Schlichting and
Smith 2002, Miner et al. 2005). In plants, phenotypic plasticity is most often expressed at the
modular level where individual shoots, leaves, and so on respond to their own local environ-
ment (de Kroon et al. 2005). Historically, much of the focus in evolutionary theory has been
on adaptive, habitat-based specialization with plasticity viewed as an alternative (the specialist
or generalist argument). This view largely ignores the important role that modular-based
expression of plasticity plays. Most of the focus in evolutionary theory has been on specializa-
tion. In fact, in heterogeneous environments, plasticity is not an alternative since it may be
required for survival, or at least for maximizing performance. Moreover, plasticity itself may
not be an alternative but a form of specialization. Although a characteristic arising as a result of
plastic phenotypic variation cannot be the subject of selective forces, the capacity to express
plastic variations in phenotype may be under genetic control and therefore subject to selection
(Scheiner and Lyman 1991, Scheiner 1993, Via et al. 1995). This is the basis for the notion that
species adapted to certain environments may have a greater capacity for acclimation than
others from other environments. The role of plasticity and especially acclimation has been most
commonly examined in terms of temporal heterogeneity. Paradoxically, they have been studied
most commonly as a static process with plants grown for a long term in different light
environments rather than as a dynamic process responding to temporal changes in the envir-
onment. Functionally, the mechanisms involved in temporal and spatial scales are similar, but
a full understanding of the temporal scale requires understanding of the dynamics of the
mechanisms and their interaction with development and not just their static end result.
Understanding the adaptive significance of plasticity of a character requires knowledge of
how it scales up to whole-plant performance. If a plastic response functions to improve
performance, most often the result at the next level up is a greater homeostasis. For example,
sun shade acclimation at the leaf level does not compensate for the large difference in resource
supply (light) between sun and shade environments, but it does result in relatively smaller
differences in carbon gain and growth between the sun and the shade than would be expected
in the absence of acclimation (Sims et al. 1994, Evans and Poorter 2001). The scaling needs to
consider not only the benefits provided by the character itself but also the costs that impact
the overall performance. Costs are often more difficult to assess and include both the direct
(construction and maintenance) and opportunity costs.
SPATIAL HETEROGENEITY IN LIGHT ENVIRONMENTS
C
AUSES AND SCALES OF SPATIAL HETEROGENEITY
Variation in overstory canopy structure is the primary factor causing the great spatial
heterogeneity evident in forest understory light environments (Figure 7.1). Forest overstory
canopies never close completely, in part because foliage tends to be clumped at the ends of
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216 Functional Plant Ecology
2500
Heliocarpus appendicula
Salacia petensis
Understory
Above
crown
Upper
crown
Lower
crown
Below
crown
Gap
38.3 mol m
−2
day
−1
9.51 mol m
−2
day
−1
3.57 mol m
−2
day
−1
Photon flux density (
µ
mol m
−2
s
−1
)
4.99 mol m
−2
day
−1
0.17 mol m
−2
day
−1
4.05 mol m
−2
day
−1
1.25 mol m
−2
day
−1
0.66 mol m
−2
day
−1
1500
2000
1000
500
0
468101214161820
2500
1500
2000
1000
500
0
468101214161820
2500
1500
2000
1000
500
0
468101214161820
2500
1500
2000
1000
500
0
468101214161820
2500
1500
2000
1000
500
0
4 6 8 1012 14161820
2500
Time
(
h
)
1500
2000
1000
500
0
4 6 8 1012 14161820
2500
1500
2000
1000
500
0
4 6 8 1012 14161820
2500
1500
2000
1000
500
0
4 6 8 1012 14161820
FIGURE 7.1 Patterns of spatial and temporal heterogeneity in the light environment as illustrated by the
diurnal courses of PFD measured with sensors located at four positions in the crowns of a pioneer
species, Heliocarpus appendicula, growing in a 900 m
2
gap and understory tree Salicaia petensis occuring
under a closed canopy. The daily integrated PFD at each sensor is given in each box. (Adapted from
Kuppers, M., Timm, H., Orth, F., Stegemann, J., Stober, R., Paliwal, K., Karunaichamy, K.S.T., and
Ortez, R., Tree Physiol., 16, 69, 1996. With permission.)
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Responses of Plants to Heterogeneous Light Environments 217
branches and because of the statistical probability that even at the maximum leaf area
index, small gaps still exist. These small gaps create sunflecks, a major source of hetero-
geneity in understory light environments. Species differences in architecture influencing
light transmission may be important in heterogeneity on a scale equivalent to a tree crown
(Canham et al. 1994, Kabakoff and Chazdon 1996, Parker et al. 2001). Understory trees
and shrubs then create further spatial heterogeneity. Crowns of adjacent trees rarely grow
into one another so that between them gaps may be more prevalent. Wind-induced
abrasion by adjacent crowns may be important in creating gaps between tree crowns
especially in areas prone to high winds (Meng et al. 2006). At the next scale-up, branch
falls create slightly larger canopy gaps. Individual tree falls create gaps from 50 to 600 m
2
depending on the size of the individual and the collateral damage. In most lowland
tropical forests, 3%–15% of the land area is in gaps at various stages of recovery (Brokaw
1985). Estimated turnover rates (mean time between gaps at any one point) range from
60 to 450 years with a mean of about 100 years. Since the effect of a gap on the light
environment extends into the adjacent understory for some distance because of lateral
light transmission, it is likely that every plant in the forest is influenced by a gap at some
time in its life cycle, and many may be influenced multiple times. Responses to even larger
scales of landscape heterogeneity caused by fire, landslides, logging, and so on are beyond
the scope of this chapter.
Within each of these scales of spatial heterogeneity, there exists a further fine-scale
heterogeneity. Sunflecks exhibit strong gradients of PFD across them because of penumbral
effects arising because of the approximately 0.278 arc diameter of the solar diskpoints from
which part of the solar disk is occluded in partial shadow or penumbra (Cescatti and
Niinemets 2004). Only when the full solar disk is visible (i.e., the gap is large enough and
the solar disk is fully within it) is an area of full, direct-beam PFD present. Penumbra, while
not changing the total PFD transmitted does even out the distribution and hence diminishes
heterogeneity. Both peak values are diminished and the area receiving partial direct PFD is
increased. The greater area means that an understory plant intercepts a sunfleck for a
longer time, and since the penumbral light is at a lower PFD than the full direct-beam
PFD, it is used more efficiently. Simulations reveal that this evening of the PFD can more
than double the assimilation due to a sunfleck in an understory plant as compared with
simulations where the sun is treated as a point source (Pearcy unpublished results). Under
tall forest canopies, most sunflecks are entirely penumbral light with maximum PFDs of
0.1–0.5 of the full, direct-beam PFD. Deep crowns with many narrow leaves such as
conifers cause most of the PFD within them to be penumbral. Under these circumstances,
total crown photosynthesis can be enhanced by 10%–17% (Ryel et al. 2001), whereas
photosynthesis of the lower branches can be enhanced by as much as 40% (Stenberg
1995). In contrast, penumbra are relatively unimportant within shoots of species with
large leaves (Pearcy et al. 2004). The spatial scale of sunfleck size has been shown to vary
from 0.1 to 1 m, still typically smaller than most plant crowns in the understory so that
often only part of the crown is influenced by a given sunfleck (Baldocchi and Collineau
1994). Variability within the sunfleck and the self-shading within the crown creates an
extremely heterogeneous light environment. Spatial autocorrelation analysis revealed that
the correlation between light readings from arrays of photo sensors decreased to 0.4 within
0.2 m and to 0.03 by 0.5 m (Chazdon et al. 1988). Cloud cover greatly reduced the spatial
heterogeneity both in the short and long terms confirming that most of the spatial hetero-
geneity was due to sunflecks. Since seasonal changes in solar angle change the location of
sunflecks, the large spatial heterogeneity observed for a day could be considerably reduced if
monthly or annual means are considered.
At the scale of a forest stand, geostatistical techniques such as spatial statistics, wavelet
analysis (Bradshaw and Spies 1992), and lacunarity analysis (Frazer et al. 2005, Sinoquet et al.
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218 Functional Plant Ecology
2005) have been employed to estimate the scales of heterogeneity of forest understory light
environments. These techniques are used to examine the inherent structure in spatial or
temporal data sets and then to infer characteristics of the processes that imposed these
patterns. For light, sampling is done along transects using hemispherical photographs or
Li-Cor plant canopy analyzers (or similar instruments). Spatial statistical techniques have
been the most widely used technique, and have revealed a significant spatial autocorrelation
in light environments at scales of 1–5 or 10 m but spatial independence at scales of 20 m or
more (Clark et al. 1996, Nicotra et al. 1999). The latter indicates a scale of sampling where
more traditional statistical approaches can be used to analyze relationships between plant
performance and light availability. In contrast to spectral analysis, which is sensitive to the
periodicity present in a data set, wavelet analysis examines whether a particular data struc-
tural shape (the wavelet) that is chosen to meet the study objectives detects the occurrence of
similar patterns at different scales in the data set (Bradshaw and Spies 1992). Lacunarity is
sensitive to the texture or gapiness such that a pattern that shows substantial heterogeneity in
the size distribution and spatial arrangement of gaps is considered to be highly lacunar
(Saunders et al. 2005).
A major goal of investigations of heterogeneity of light environments is an understanding
of the role of light availability in species diversity and forest regeneration. Numerous experi-
mental studies have demonstrated a strong positive effect of light availability on seedling
physiological and growth performance. From this lead, theoretical studies have proposed
strong links between the spatial heterogeneity in light environments and tree regeneration
(Pacala et al. 1996, Bolker et al. 2003). However, measurements reveal a much more complex
pattern. For example, although spatial patterning at similar scales was found in both light
availability and in seeding distribution in a Costa Rican tropical forest, the relationship
between light availability and seedling abundance was weak and inconsistent, with as often
as not a negative relationship (Montgomery and Chazdon 2001). Nevertheless, the similar
scales of autocorrelation in seedling abundance and light availability suggest a more direct
relationship. This could be explained if current patches of greater seedling abundance
reflect former patches of greater light availability. However, patterns of seed rain, herbivory,
and other factors may also act to obscure the relationship between light availability and
seedling abundance.
Although the central portion of larger tree fall gaps may receive a relatively uniform
light environment, pronounced gradients occur around the edges because of the shading
by adjacent canopy trees. In the Northern Hemisphere, the southern edge of a gap is
shaded even at midday, whereas the northern edge receives the full direct beam solar
radiation. Similarly, the west side receives sunlight in the morning whereas the east side
receives it in the afternoon. The west side of the gap should therefore be more favorable
for photosynthesis because of the lower temperatures and higher humidities in the morning.
Wayne and Bazzaz (1993a,b) found differences in the diurnal course of photosynthesis on
the east and the west sides, and small differences in the daily carbon gain, but no differences
in growth. Because of variations in solar elevation angle, the effect of gap geometry on the
heterogeneity of light regimes in them depends on season and latitude. Gap size and
geometry influence the total daily PFD available for photosynthesis because of the effect
on hours of direct PFD and the transmission of diffuse PFD. Plants on the gap edges are
usually in strongly directional light environments because of shading by adjacent plants on
one side.
Within individual crowns, gradients of PFD occur within canopies because of self-shading,
which depends on crown density and the spatial pattern of leaves and stems. Most of the foliage
in tree crowns are concentrated in an outer 0.5–1 m thick shell, which may intercept 90% of
the incident radiation. Therefore, gradients of available PFD in these crowns are quite steep
(see Figure 7.1).
Francisco Pugnaire/Functional Plant Ecology 7488_C007 Final Proof page 219 30.4.2007 7:57pm Compositor Name: DeShanthi
Responses of Plants to Heterogeneous Light Environments 219