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The Behavioral Ecology of
Nutrient Foraging by Plants
James F. Cahill Jr and Gordon G. McNickle∗
Department of Biological Sciences, University of Alberta, Edmonton AB T6G 2E9, Canada;
email: [email protected]
Annu. Rev. Ecol. Evol. Syst. 2011. 42:289–311
Keywords
First published online as a Review in Advance on
August 29, 2011
root ecology, plant behavior, plant-soil interactions, plant strategies,
precision, resource selection functions
The Annual Review of Ecology, Evolution, and
Systematics is online at ecolsys.annualreviews.org
This article’s doi:
10.1146/annurev-ecolsys-102710-145006
c 2011 by Annual Reviews.
Copyright All rights reserved
1543-592X/11/1201-0289$20.00
∗
Current address: Department of Biological
Sciences, University of Illinois, Chicago,
Illinois 60607; email: [email protected].
Abstract
Foraging for resources influences ecological interactions among individuals
and species, regardless of taxonomic affiliation. Here we review studies of
nutrient foraging in plants, with an emphasis on how nutritious and nonnutritious cues in the soil alter behavioral decisions and patterns of root
placement. Three patterns emerge: (a) Plants alter root placement in response to many diverse cues; (b) species respond differently to these cues;
and (c) there are nonadditive responses to multiple cues, indicating that plants
exhibit complex multidimensional root foraging strategies. We suggest that
this complexity calls for novel approaches to understanding nutrient foraging by plants. Resource selection functions are commonly used by animal
behaviorists and may be useful to describe plant foraging strategies. Understanding such approaches may allow researchers to link individual behavior
to population and community dynamics.
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INTRODUCTION
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Behavior: we
use Silvertown &
Gordon’s (1989)
definition of behavior,
“what a plant or animal
does, in the course of
an individual’s
lifetime, in response to
some event or change
in its environment”
Foraging: the
act of searching for,
acquiring, and
consuming resources
Proximate and
ultimate: the
ethologist Nikolaas
Tinbergen suggested
that forms of questions
for any behavior could
be classified as: the
proximate (i.e.,
evolutionary)
mechanisms of
adaptation and
evolution
Cue: a specific signal
that can induce a
behavioral response
Behavioral type:
within a genotype or
species, a consistent
behavioral response to
a specific cue
The idea that plants behave is well established in the literature (Hutchings & de Kroon 1994,
Karban 2008, Lacey & Herr 2005, Marshall & Folsom 1991, McNickle et al. 2009, Silvertown
& Gordon 1989). Among the best-studied aspects of plant behavior is that of root foraging for
soil nutrients. Finding and acquiring resources are fundamental aspects of the ecology of organisms, and behaviorists have studied both proximate and ultimate (sensu Tinbergen 1963) aspects
of foraging ecology (Stephens et al. 2007). Behaviorists recognize that individual decisions are
influenced by genetic constraints as well as multiple local cues (e.g., resources, predators). Here
we define the combined responses to multiple cues as an organism’s foraging strategy. Variation
in foraging strategies among individuals and species can have consequences for population and
community dynamics. To facilitate understanding, we provide a critical review of these behavioral
aspects of plant foraging for nutrients.
Plant root growth is plastic, resulting in nonuniform root distributions in soil. Some of this
plasticity results from the plant’s condition (e.g., size, health), whereas some results from soil
factors such as resource distributions or neighbor presence (Hodge 2004, Hutchings & de Kroon
1994, Kembel & Cahill 2005, Robinson 1996). Because resource capture influences the energetic
and nutritive state of organisms, behaviorists recognize the importance of understanding foraging
ecology (Stephens et al. 2007) in order to understand broader aspects of an organism’s life history
and general ecology. Such a need for this understanding is just as strong for plants as for animals.
Roots drive many ecological interactions and processes (e.g., Casper & Jackson 1997,
Hutchings & de Kroon 1994, Wardle 2002). They can account for the majority of plant biomass
in many communities ( Jackson et al. 1996), and a significant proportion of photosynthate is fed
to fungi and bacteria that live in and around plant roots (Wardle 2002). Furthermore, competition for nutrients often reduces plant growth more than competition for light (e.g., Casper &
Jackson 1997). The dynamics of competition experienced by individual plants can be influenced
by the spatial distribution of roots among co-occurring plants. However, processes that determine
where plants put their roots within the soil are poorly understood in comparison with analogous
aboveground processes in plants (de Kroon & Hutchings 1995) and movement patterns among
vertebrates.
Here we review the behavioral ecology of plant root foraging for nutrients, linking local decisions to larger-scale impacts. We focus primarily on the behavioral aspects of root growth and
placement, although many other aspects of nutrient foraging are also important (e.g., uptake kinetics). A review on this subject is timely, as we believe recent conceptual (de Kroon et al. 2009,
Dudley & File 2007, Forde 2009, Gersani et al. 2001, Hodge 2009, McNickle & Cahill 2009,
Novoplansky 2009) and technological (Table 1) advances will allow for rapid advancements in
our understanding of plant foraging behavior. Here we provide a critical assessment of current
knowledge to guide and stimulate future research. Specifically, we focus on two questions:
1. What soil factors influence where an individual plant places its roots in the soil?
2. How do root distribution responses to multiple factors combine to describe a plant’s overall
foraging strategy?
The first question is analogous to understanding where on a landscape a cougar hunts or an elk
feeds as a function of resource distributions. There exists substantial interspecific variation in root
placement relative to cues, suggesting adaptive benefits of different behavioral responses under
different conditions (see below). Building on the terminology of Sih (2004b), we refer to consistent
differences among individuals or species in response to specific cues as alternative behavioral types.
In the first section of this review, we examine the evidence for alternative behavioral types in
response to numerous nutritious and non-nutritious cues.
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Table 1 Comparison of some methods used to study root distributions and species identification, along with representative
references
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Method
Description
Advantages
Disadvantages
Excavation
Whole root systems or plots
are excavated by digging
Generates large
amounts of
high-quality data;
provides identification
to the level of the
individual and species
Often misses fine roots
destroyed by digging;
extremely laborious and
impractical for experiments
with many treatments or
substantial replication
Brisson & Reynolds
1994
Reference(s)
Root chambers,
rhizotrons,
and
minirhizotrons
Roots are observed/
photographed through
clear material sunk in the
ground, in rooting
chambers, or through
tubes buried in soil
Adaptable to
greenhouse and field
studies; can be
inexpensive
Measures a tiny fraction of
the root volume, in two
dimensions; hard to identify
roots to the individual or
species; time-consuming
image analysis
Fransen & de Kroon
2001, McNickle &
Cahill 2009,
Mommer et al. 2010
Staining
Colored dye is injected into
the root system of focal
plants, with identification
based on color; can be
used in combination with
imaging
Inexpensive
Difficult to get reliable and
even staining of roots; labor
intensive
Cahill et al. 2010,
Schenk et al. 1999
Fluorescence
and reflectance
Roots are exposed to
different wavelengths of
light; differential
fluorescence (ultraviolet)
or reflectance indicates
species identity
Can allow for
determination of
relative abundance;
once equipment
purchased,
inexpensive
Poorly understood and
highly species/system
specific; time-consuming
image analysis
Caldwell et al. 1991a,
Roumet et al. 2006
Tracers
Rare elements or
compounds are injected
into soil; shoots are
analyzed, and the
positioning of roots is
inferred from elevated
shoot concentrations
Can provide
identification to the
level of the individual
or species; relatively
simple
Low number of tracers
restrict the number of soil
locations measured; does
not differentiate between
root and fungal uptake;
chemical analyses are
relatively expensive
Casper et al. 2000
Morphological
identification
Roots are collected and
identified based on
morphological differences
Can provide
identification to the
level of species;
inexpensive
Intraspecific variation in root
morphology limits
interspecific comparisons;
identification of individuals
is not possible; timeconsuming
Gasson 1979
Molecular
identification
Species are determined
from root tissue on the
basis of DNA markers
Can provide
identification to the
level of individual or
species; increasingly
inexpensive
Requires expertise with
molecular techniques;
high-throughput methods
still being developed;
typically results in
presence/absence data, not
relative abundance
Bobowski et al. 1999,
Brunner et al. 2001,
Jackson et al. 1999,
McNickle et al.
2008, Moore &
Field 2005, Ridgway
et al. 2003, Taggart
et al. 2010
(Continued )
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Table 1 (Continued )
Method
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Quantitative
polymerase
chain reaction
(PCR)
Description
Similar to molecular
identification, except
quantitative real-time
PCR is used to identify
species and estimate
relative abundance
Resource selection
function (RSF): any
statistical model that
gives the probability
that a unit of habitat
will be used by an
organism based on
biotic or abiotic
variables associated
with each habitat unit
Advantages
Can provide
species-specific
abundance and
distribution data from
samples
Disadvantages
Requires a reliable internal
standard to obtain useful
quantitative PCR data;
limited to a low number of
species per sample;
expensive
Reference(s)
Mommer et al. 2008,
2010
The second question is concerned with the effects of multiple cues on root distributions.
Behavioral types may not be an additive combination of responses to isolated cues, and therefore we
discuss integrative nutrient foraging strategies. We introduce resource selection functions (RSFs),
a statistical approach often used to model habitat selection and describe patterns of occupancy
by foraging animals, as one potential tool to describe root placement in response to multiple
cues. Such information is critical to inferring and defining alternative nutrient foraging strategies
among individuals and species.
Finally, we present a conceptual overview of how plant foraging for nutrients can be integrated
into existing frameworks linking plant traits to larger-scale processes. We also highlight specific
areas we believe are the highest priorities for additional research in the general area of plant
foraging.
WHAT SOIL FACTORS INFLUENCE WHERE AN INDIVIDUAL
PLANT PLACES ITS ROOTS IN THE SOIL?
Since the work of Drew (1975) and Drew et al. (1973), it has become apparent that nutrients serve
as cues influencing root growth and placement. Consequently, root system architecture is highly
dynamic, developing partially in response to the spatial distribution of nutrients in the soil (e.g.,
Hodge 2004). However, roots also respond to non-nutritious cues in the soil, such as soil biota
and neighboring plants (e.g., Cahill et al. 2010, Stevens & Jones 2006). Combined, this evidence
suggests that plants exhibit fine-scale variation in lateral and vertical root distributions, driven
in large part by behavioral response to diverse soil cues. In this section we review the different
cues that may impact root placement in soil and the alternative behavioral types that exist among
species. However, we begin by emphasizing that not all variation in root growth and placement is
necessarily the outcome of behavior.
Stochastic Influences on Root Development
Genetically identical plants grown under environmentally uniform conditions do not necessarily produce identical patterns of root placement; instead they may exhibit stochasticity in root
placement (reviewed in Forde 2009). This random variation in root development has been called
developmental instability (Forde 2009), and its effects on root architecture can be substantial, even
surpassing variation induced by environmental cues (Forde 2009).
The degree to which a plant’s root system architecture is influenced by stochastic processes has
a genetic component, suggesting that natural selection may shape developmental instability (Forde
2009). The potential benefits of random processes as part of a nutrient foraging strategy are not
known, although it may reduce intraplant root competition and increase the probability of finding
localized nutrient patches (Forde 2009). Animal behaviorists have developed an extensive literature
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focusing on the costs and benefits of alternative search strategies that incorporate varying degrees
of stochasticity in searching (Smouse et al. 2010). Analogous research linking root placement and
stochastic search processes is lacking. Differentiating among stochastic and behavioral causes of
root placement patterns remains a challenge.
The spatial distribution of soil nutrients varies at scales smaller ( Jackson & Caldwell 1993) and
larger than the size of a single plant. Here we focus on variation at scales within the soil potentially
explored by an individual plant, as this is most relevant for behavioral decisions. The effects of
resource distributions on root placement have been reviewed elsewhere (e.g., de Kroon et al. 2009;
Hodge 2004, 2006, 2009; Hutchings & de Kroon 1994; Kembel & Cahill 2005; Reynolds & Pacala
1993), and thus we focus on a behavioral interpretation of these responses.
When nutrients are heterogeneously distributed in soil, plants generally place more roots
into locations with higher nutrient concentrations (Hodge 2004, Kembel & Cahill 2005). This
behavioral response has been referred to as root proliferation and foraging precision (Hodge 2004,
Kembel & Cahill 2005). We prefer the latter term, as it focuses on the general behavior, rather
than a developmental response. For example, foraging precision can be expressed developmentally
either through increased root growth (e.g., Bilbrough & Caldwell 1995) or through decreased root
mortality (Gross et al. 1993), but the resulting behavior is identical.
Although most species alter root placement in response to nutritious cues (Kembel & Cahill
2005), there is substantial interspecific variation (Figure 1). Some species place a large proportion
of roots in nutrient patches, exhibiting the behavioral type of high precision (sensu Campbell et al.
1991). Other species have a more muted response to the same resource cues (Figure 1), displaying the behavioral type of low precision. Some variation in behavioral type is phylogenetically
1.0
0.9
0.8
Precision
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Root Responses to Nutritious Cues
0.7
Monocots
0.6
0.5
0.4
Eudicots
0.3
0
20
40
60
80
100
120
Rank
Figure 1
Rank order of 120 species’ foraging precision measured as the proportion of fine roots in nutrient-enriched patches (0.5 = random
root growth with respect to nutrient distributions; 1.0 = all roots are located in nutrient-enriched patches). Monocots are indicated by
dark green circles and eudicots by light green circles. Data are from Kembel & Cahill (2005).
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conserved, as eudicots generally have higher precision compared with monocots (Kembel &
Cahill 2005). Nutritious cues also influence uptake kinetics (Fransen et al. 1999) and root demography (Gross et al. 1993), although these are not as well studied, and we do not discuss them here.
An additional limitation in the available data is our lack of understanding functional and behavioral
differences due to distributions of different forms (e.g., NO3 versus NH4 ) and types of nutrients
(e.g., nitrogen versus phosphorus), as well as differences among soil nutrients and water. Existing
studies tend to either manipulate resources as a group by adding NPK or organic fertilizers, or
they tend to manipulate a single nutrient, without comparing responses to other nutrients. Such
tendencies may reflect the chemistry of natural nutrient patches (e.g., urine patches) and yield
the most useful ecological information, but they also limit the ability to draw conclusions about
potential behavioral responses to specific compounds.
In addition to changes in root placement, nutritious cues can influence the transient processes
of root growth. For example, consistent with a prediction of the marginal value theorem (Charnov
1976), the presence of nutrient-rich patches can cause plants to reduce their rate of soil exploration
such that they leave nutrient-rich patches later than they leave nutrient-poor patches (McNickle
& Cahill 2009). Plants can also respond to nutrient patches through reduced root mortality (Gross
et al. 1993), also resulting in a pattern of increased residency in nutrient-rich patches relative
to nutrient-poor patches. Few species have been screened for the transient processes described
here, and as a result, the generality of these behaviors and potential alternative behavioral types
are unknown.
At the individual level, it is critical to know which specific traits directly influence resource
capture and may be subject to optimization. Several cost-benefit approaches to understanding
root growth have been proposed, including a focus on the resource capture efficiency of individual
roots (Eissenstat et al. 2000) and whole-plant fitness as a function of alternative allocations to
individual nutrient foraging strategies (McNickle et al. 2009). However, data explicitly testing
these possibilities are lacking. Instead, there has been a general assumption that root biomass
distributions are reflective of functionally important behavioral decisions. This assumption has
generally not been challenged with data.
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Molecular Mechanisms of Root Placement
Most of our understanding of the genes that govern root placement comes primarily from studies
of Arabidopsis thaliana. As a result, the molecular basis to explain the breadth of alternative behavioral types known to exist (Figure 1) is lacking. Root placement can be influenced by root-level
responses to the local conditions and, under some circumstances, systemic modulation of the response depending on the whole-plant status (Bilbrough & Caldwell 1995, Cui & Caldwell 1997,
de Kroon et al. 2009). Genes that act as nitrate sensors and multigene pathways that influence
both root growth rate and the production of secondary lateral roots have been described (Chen
et al. 2008, Chrispeels et al. 1999, de Kroon et al. 2009, Forde 2009, Forde & Walch-Liu 2009).
These mechanisms are partially involved in plant responses to nutritious cues (nitrate patches).
However, the molecular biology of root proliferation is complex (Forde & Walch-Liu 2009) and
involves more root-specific and generalized physiological pathways than are currently understood
(de Kroon et al. 2009). More thorough reviews on this topic have been provided elsewhere (Chen
et al. 2008, Chrispeels et al. 1999, de Kroon et al. 2009, Forde 2009, Forde & Walch-Liu 2009).
Responses to Non-Nutritious Cues
Soil contains more than nutrients and is also home to competitors, pathogens, mutualists,
and herbivores. This is the ecological context in which plant root foraging behavior evolved
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(Hodge 2001, Hodge et al. 2000b, Robinson et al. 1999, Stevens & Jones 2006), and it is expected
that root distributions reflect not only resource distributions but also the distributions and pressures of these other factors. Although responses to non-nutritious cues are relatively understudied,
in the subsequent sections we review what is known about the impacts of neighbors, mutualisms,
and predation/pathogens on root placement by plants.
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Root Placement Responses to Neighboring Plants
Competition typically reduces the total amount of roots that plants have available to place in soil
(Casper & Jackson 1997, Goldberg et al. 1999) as well as the fine-scale distribution of roots that are
produced (Novoplansky 2009). Litav & Harper (1967) suggested three ways that competition may
influence the spatial distribution of the roots of competing plants: random mixing, undermixing,
and overmixing. Which pattern occurs depends on each plant’s response to roots of con- and
heterospecific neighbors. Building on the concepts of Litav & Harper (1967), we suggest there
are three behavioral types that may occur in response to the presence of neighbors: no response,
avoidance, and aggregation.
With the behavioral type of no response, plant root placement would be constant (relative
to shoot size) regardless of the presence or identity of neighboring roots. Importantly, such a
pattern can occur either because a plant is unable to detect/respond to neighbors or because
plants do respond to neighboring roots but do not differentiate between their roots and those of
different individuals in the soil. If all co-occurring individuals demonstrate this behavioral type,
then random mixing of roots should occur at the plot level (sensu Litav & Harper 1967). This
behavioral type has been documented (Litav & Harper 1967, Mommer et al. 2010). However,
in a study involving five species, Litav & Harper (1967) found that two species expressed no
response under low-nutrient conditions and expressed avoidance under high-resource conditions.
This switch in behavioral type emphasizes a recurrent theme in this review: The behavioral type
expressed by an organism in response to one cue is often contingent on the presence or absence
of other cues in the soil environment.
Aggregation occurs if plants increase root growth in the vicinity of neighbor roots. This idea,
also referred to in the literature as over-proliferation (sensu Gersani et al. 2001), has been formalized for annual plants with simple game theoretic models that assume plants compete only for
resources by increasing root biomass. The resulting evolutionary stable strategy is the behavioral
type of aggregation, even when this increased root production comes at the cost of root growth in
unoccupied soil and reduced fitness (Gersani et al. 2001; O’Brien & Brown 2008; O’Brien et al.
2005, 2007). Many empirical studies that claim to demonstrate aggregation (e.g., Gersani et al.
2001, Maina et al. 2002, O’Brien et al. 2005) have confounded soil volume and soil fertility in
their experimental designs, resulting in sharp criticism (Hess & de Kroon 2007, Laird & Aarssen
2005, Schenk 2006). Although this critique has been contested (O’Brien & Brown 2008), data are
limited. However, if the behavioral type of aggregation occurs, overmixing of roots should occur
in locations of root overlap among neighboring plants. Depending on compensatory responses
elsewhere in the root system, overmixing, undermixing, or random root distributions could be
found at the whole-plot level.
The third behavioral type expressed in response to neighbor roots is avoidance, which results
in the undermixing (i.e., segregation) of roots. Schenk et al. (1999) identified 13 species of plants
from 16 studies that exhibited some degree of root segregation, with additional examples found
in more recent studies (e.g., Cahill et al. 2010, Gersani et al. 1998, Holzapfel & Alpert 2003, von
Felten & Schmid 2008). However, undermixing can occur both through avoidance and through the
suppression of root growth of one plant by another (e.g., allelopathy) (Mahall & Callaway 1991).
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Table 2 A summary of the environmental cues discussed in this review and how they influence individual root foraging
behaviors in plantsa
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Effect on root placement
Species studied
Reference(s)
Developmental
instability
Genetically identical plants may
place roots differently owing to
developmental instability
Arabidopsis thaliana, Zea mays
Reviewed in Forde 2009
Resources
Plants generally preferentially
place roots into nutrient-rich
patches
>120 species (see Kembel & Cahill
2005 for an extensive list)
Reviewed in Hodge 2004, 2006,
2009; Hutchings & de Kroon
1994; Kembel & Cahill 2005;
Robinson 1996
Plant neighbors
No response: Plants do not adjust
root placement in response to a
neighbor
Pisum sativum, Avena sativa
(N-poor soil only), Leucanthemum
vulgare, Plantago lanceolata
Litav & Harper 1967, Mommer
et al. 2010
Aggregation: Plants increase root
growth near a neighbor; often
called overproliferation
Glycine max, Phaseolus varigaris,
P. sativum, Anthoxanthum
odoratum, Festuca rubra
Gersani et al. 2001, Maina et al.
2002, Mommer et al. 2010,
O’Brien et al. 2005
Avoidance: Plants reduce growth
near a neighbor; one of several
mechanisms leading to root
segregation
Pisum sativum, A. sativa (N-rich
soil only), Cakile edentula, Abutilon
theophrasti, and 13 more species
from 19 papers reviewed in
Schenk et al. (1999)
Cahill et al. 2010, Dudley & File
2007, Gersani et al. 1998, Litav
& Harper 1967, Schenk et al.
1999
Microbe neighbors
Too few data
Other mutualists
Too few data
Soil enemies
Too few data
a
Table limited to studies cited in the main text.
These are fundamentally different responses and highlight that the description of root patterns in
soils cannot by itself indicate the specific behaviors expressed by co-occurring plants.
The current evidence in the literature seems to suggest that avoidance may be the most common behavioral type in response to neighbor cues (Table 2). However, we caution that it is usually
not clear in these studies whether avoidance is caused by behavioral shifts of one plant in response
to another or whether it is an outcome of chemically mediated interference competition. Furthermore, the literature also shows that all three behavioral types do exist and that an individual
species may express multiple behavioral types depending on other cues.
Unfortunately, although many studies document patterns of root placement among cooccurring plants (e.g., Table 2), few studies directly observe changes in the root growth of one
plant in response to roots of another (e.g., Cahill et al. 2010, Mahall & Callaway 1991, Mommer
et al. 2010). Such studies are logistically challenging but are critical to draw conclusions about the
specific behaviors plants express, rather than simply describing the pattern of root distributions
that are the outcome of behavioral changes. We anticipate that recently developed molecular
methods should facilitate this type of research (Table 1).
Kin and Self-Recognition
Another factor that can influence behavioral responses to neighboring roots is the relatedness of
the potential competitor. For example, simulations of root system development typically include
parameters that minimize the potential for overlap among intraplant (complete genetic similarity) roots (e.g., Lynch et al. 1997). Such decisions are supported by empirical observation, as
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plants show differential patterns of root placement in response to neighboring roots dependent
on whether the neighbors are part of the same connected genet or whether connections have been
severed (Falik et al. 2003, Gruntman & Novoplansky 2004).
There is also evidence for kin recognition by plant roots (Dudley & File 2007), suggesting that
a plant could enhance inclusive fitness by minimizing competition with close relatives (Hamilton
1964a,b). For example, Dudley & File (2007) found that Cakile edentula reduced competition with
kin through the production of less fine root biomass compared with when plants competed with
unrelated individuals. Similar results have been found for Impatiens pallida (Murphy & Dudley
2009). More studies are needed to understand the generality of these responses.
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Root Placement Responses to Microbial Competitors
Plants compete with fungi and bacteria for soil nutrients (e.g., Schimel & Chapin 1996). Furthermore, microbial community composition can demonstrate substantial variation at scales smaller
than an individual root system (Franklin & Mills 2003), suggesting that the effects of plantmicrobial interactions also vary at small spatial scales (Hodge et al. 2000a). Thus we would expect
that microbial distributions would be another cue influencing root placement, perhaps leading to
behavioral types of no response, aggregation, and avoidance with respect to microbes. However,
limited data exist that describe the fine-scale dynamics of plant-microbe competition (Hodge et al.
2000a), particularly in the context of plant behavioral responses. Given the frequency with which
plant-microbe interactions occur, this is an area that requires attention.
Root Placement Responses to Mutualists
Symbioses with mycorrhizal fungi (and to a lesser extent nitrogen-fixing bacteria) are common
among plants. These associations are generally viewed as mutualisms yielding nutrient benefits
to the plant at the expense of carbon to feed the microbes (Smith & Read 1997). However, these
interactions can vary from parasitism to mutualism as a function of nutrient conditions ( Johnson
et al. 1997). Consequently, there is reason to expect that mycorrhizal interactions will alter aspects
of plant nutrient foraging behavior.
A handful of studies have measured whether mycorrhizae alter a plant’s foraging precision in response to nutritious cues. For example, Hodge (2001) grew Plantago lanceolata in soil
with heterogeneous soil nutrients, both with and without arbuscular mycorrhizal fungi (AMF).
Although plants increased root births when AMF were present, foraging precision (as measured as
root length inside a nutrient patch) was unaffected. Similarly, Wijesinghe et al. (2001) found that
none of six herbaceous species altered foraging precision as a function of plant mycorrhizal status.
However, we caution that more experimentation is required that integrates nutrient foraging and
mycorrhizal status, particularly for aspects of foraging other than precision.
Even if foraging precision is unaltered by mycorrhizal colonization, plants infected by mycorrhizal fungi tend to decrease root length and root density at the level of the whole plant (e.g., Cui &
Caldwell 1996, Hetrick 1991, Schroeder & Janos 2005). Such responses indicate that conventional
measures of nutrient foraging (e.g., biomass allocation) are influenced by mycorrhizal status, even
if other aspects of nutrient foraging (e.g., precision) are not.
Mycorrhizal associations are often described as mutualisms that are less costly to plants than
the production of fine roots (Fitter 1991) and in which fungal hyphae serve as simple extensions of
plant roots (Cui & Caldwell 1996, Tibbett 2000). However, mycorrhizal fungi are not simply root
extensions, but are complex organisms that are themselves shaped by natural selection (Helgason &
Fitter 2009). These fungi acquire their own mineral nutrition through their own foraging behavior
(Hodge & Fitter 2010), and we cannot assume that behavioral shifts in plants are necessarily to the
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benefit of the infected host plant. We suggest that viewing mycorrhizae as examples of reciprocal
parasitism (e.g., Egger & Hibbett 2004) rather than root extensions may result in novel insight
into plant foraging behavior. Under this model, there is explicit recognition of conflicts of interest
among the partners engaged in a potential mutualism and that selection acts on individuals, and
not on the interaction as a whole. Adapting this view may help in the identification of similarities in
behavioral responses to putative mutualists, as well as traditional root parasites (described below).
Root Placement Responses to Enemies
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Root enemies consist of herbivores and pathogens/parasites from a variety of taxa and have the
potential for direct and indirect effects on root growth (Wardle 2002). Differential patterns of
root placement can occur (a) if root herbivores show preference for which roots they consume
or (b) if plants alter root growth as a function of the distribution of enemies in the soil. As
root enemies feed, they can consume roots and reduce the local abundance of roots, altering
root distributions (Stevens & Jones 2006, Stevens et al. 2007). For example, if root herbivores
demonstrate a numerical response to root densities (e.g., distribute according to an ideal free
distribution), their feeding should homogenize root distributions within the soil. However, it will
be difficult to separate the effects of plants altering root placement from the effects of soil enemies
consuming roots, thereby altering locations that contain roots. How plants modify root placement
in response to soil enemies is poorly understood.
Plants can detect belowground enemies, although what plants do with that information is
not always clear. For example, Zea mays is capable of detecting the root herbivore Diabrotica
virgifera virgifera, responding by releasing (E)-β-caryophyllene and thereby attracting predatory
nematodes (Rasmann et al. 2005). However, critical to understanding behavioral responses to
soil enemies are data documenting the dynamics of root growth. Unfortunately, most studies
involving soil enemies (Table 2) focus on total root biomass at the end of the experiment, a
pattern caused either by behavioral decisions of the plant or by consumption of the plant by the
herbivore/pathogen. Once again, we suggest that measures of the transient aspects of root growth
and placement are needed to understand the behavior of plant root foraging.
HOW DO ROOT DISTRIBUTION RESPONSES
TO MULTIPLE FACTORS COMBINE TO DESCRIBE
A PLANT’S OVERALL FORAGING STRATEGY?
Two patterns emerge in how root placement is influenced by nutritious and non-nutritious cues:
(a) Root placement is altered by multiple environmental cues (Table 2), and (b) plant species
are variable in the behavioral types they exhibit (Figure 1 and Table 2). However, behavioral
responses to multiple environmental cues can be nonadditive, generating complex multidimensional behavioral strategies. Consequently, the population- or community-level consequences of
a particular soil factor (e.g., soil nutrient heterogeneity), mediated through behavioral responses,
are context dependent.
Conditionality of Behavior
Behavioral types expressed in response to some soil cues (e.g., soil enemies, mutualists) have
received limited attention. Consequently, the effects of multiple, simultaneous cues on root placement are generally difficult to predict. However, we can draw from the concepts of trade-offs and
constraints to provide a context in which to begin to understand the available information. At a basic
level, if a plant’s response to multiple cues were additive, we would expect to find consistent effects
of those cues across different conditions (i.e., no statistical interaction in a factorial experiment).
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The best-studied examples of plant foraging responses to multiple cues come from experimental manipulations of resource distributions and neighbors (Hutchings et al. 2003). If plant
responses to nutritious cues are constant, soil nutrient heterogeneity might be expected to have
consistent additive effects on larger-scale measures, such as population biomass, regardless of
the levels of other factors (e.g., density of neighboring plants). Such consistency is not apparent, indicating context dependency on the expression of behavioral type (i.e., statistical interaction). For example, in two separate studies, Cardamine hirsuta was grown under varied conditions,
including (a) different levels of soil nutrients, (b) varying levels of nutrient heterogeneity, and
(c) different growth durations (Day et al. 2003a,b). Under one set of conditions (intermediatesized nutrient patches, plants grown for a short duration), population growth in heterogeneous
soil was 33% less than in homogeneous soils. Under another set of conditions (low nutrients, large
patches), population growth in heterogeneous soil was 250% greater than in homogeneous soil.
Context dependency is also found in two studies with Abutilon theophrasti. In these studies, nutrient
heterogeneity reduced population biomass 17% relative to homogeneous controls under one set
of conditions (intermediate density, small and infrequent patches) but increased productivity by
44% under another combination of conditions (intermediate density, small and frequent patches)
(Casper & Cahill 1996, 1998). Clearly, nutrient heterogeneity may have nonadditive effects on
population growth, even within a single species.
Underlying these varied responses of populations to soil nutrient heterogeneity are changes
in individual plant responses to soil cues, and these individual responses are also nonadditive. For
example, Cahill et al. (2010), also working with Abutilon theophrasti, combined root staining with
images from a minirhizotron camera to directly observe root distributions in relation to both the
location of soil patches and the presence or absence of neighbors. Under homogeneous nutrient
conditions, this species exhibited strong root avoidance. However, soil nutrient heterogeneity reduced the magnitude of root avoidance, resulting in the overlapping of neighboring roots within
nutrient-rich patches (Cahill et al. 2010). Other work on A. theophrasti using nonradioactive tracers
(Table 1) indirectly showed that plants accessed resources at greater distances under heterogeneous than homogenous conditions (Casper et al. 2003). If root distributions alter individualand population-level resource uptake and growth, these shifts in behavioral responses to soil cues
likely contributed to the complex population-level responses seen in other studies (e.g. Casper &
Cahill 1996, 1998).
Although less well studied, there is also evidence of conditional responses to non-nutritious
cues. For example, Semchenko et al. (2007) observed the altered root placement of two species in
response to a neighbor as a function of the species identities of both the focal and neighbor plant.
They found that neighbor identity had no effect on Glechoma hederacea roots, whereas Fragaria
vesca roots were stimulated by inter- but not intraspecific neighbors. There is also evidence that
the benefits of mycorrhizal colonization can be most strongly expressed when plants compete
for soil resources with other plants, rather than when grown individually (Hodge 2003). Thus,
although there are few studies, a contingent behavioral response when presented with multiple
soil cues appears to be common. This indicates a need for a multi-cue approach to understand
plant foraging behavior and the resulting consequences.
Using Theory to Understand Plant Foraging Behavior
In the face of such empirical complexity, it would be helpful to turn to plant-based theory for
a foundation on which to rest the contrasting results. However, the study of plant behavior
has been primarily driven by empiricists rather than theoreticians. This has resulted in a situation in which the theoretical aspects of plant behavior are relatively underdeveloped both
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in comparison with that of animal behavior and in comparison with the number of existing
experiments.
The most extensive work in plant behavioral theory relates to clonal plants and the optimal
placement of daughter plants in a variable environment (de Kroon & Hutchings 1995). For example, using simulation modeling, Sutherland & Stillman (1988) described how soil conditions should
favor particular behavioral types, such as reducing internodal length in areas of high-resource availability. Simulation modeling of nonclonal plants has also been used to show potential benefits of
root responses to localized nutrient patches ( Jackson & Caldwell 1996, Ryel & Caldwell 1998).
Similarly, de Kroon & Hutchings (1995) recognized the linkages between plant modularity
and the fine-scale placement of roots and emphasized the need to develop a foraging theory that
could apply to all plants. The identification of plant modularity as critical to understanding how a
plant interacts with its environment is old (White 1979); however, the importance of this insight
is essential for the development of any theory for plant foraging behavior (de Kroon et al. 2005,
2009; McNickle et al. 2009).
Some studies have combined animal-derived theory to generate and test qualitative predictions about root placement in soil. For example, root placement in response to nutritious cues
has been placed in the theoretical framework of optimal patch-use models, such as the marginal
value theorem (Gleeson & Fry 1997, McNickle & Cahill 2009, McNickle et al. 2009). Similarly,
alternative responses to neighbor roots have been addressed as a competitive game (Gersani et al.
2001; O’Brien & Brown 2008; O’Brien et al. 2005, 2007), viewed as a problem of kin selection
(Dudley & File 2007, Murphy & Dudley 2009), and discussed in the context of ideal free distributions (Gersani et al. 1998). An alternative to game-theoretic approaches is that of swarming, with
individual root tips acting as individuals (Baluska et al. 2010).
Despite these examples, there have been few attempts to generate theory about how multiple
behaviors may interact to influence whole-plant foraging strategies, individual fitness, and consequences at the population and community levels. Instead, we are left with a (small) number
of theoretical works devoted to single behavioral types, on one hand, and a (small) number of
empirical studies that demonstrate that the behavioral type expressed by a plant can be contingent
on multiple cues in the soil, on the other hand. We suggest that a critical missing piece in the
literature is a conceptual understanding of how individual behaviors combine to form behavioral
strategies, along with the related plant-specific theory.
Developing Foraging Strategies
Root foraging
strategy: the
combination of
behavioral types
expressed by an
individual plant in
response to multiple
cues related to root
foraging
300
Here we define a plant’s nutrient foraging strategy as the combination of behavioral types expressed
by an individual plant in response to multiple cues related to nutrient foraging. For example,
how a plant responds to nutrient heterogeneity (degree of precision) defines one behavioral type
(Figure 1). How that same individual responds to neighbors (no response, avoidance, aggregation) defines a second behavioral type (Table 2). That individual’s foraging strategy is defined by
the combination of these behavioral types (e.g., avoidance + high precision), along with behavioral types induced by other cues (e.g., mutualists, enemies) and any contingencies in behavioral
expression (e.g., avoidance only in homogeneous soils).
Although there have yet to be explicit efforts to describe a plant’s overall nutrient foraging
strategy, the idea that nutrient foraging behaviors should be integrated into a general understanding of interspecific variation in life-history strategies has been previously suggested. For example,
Grime et al. (1997) included foraging precision as one variable describing a plant’s overall lifehistory strategy. Foraging precision (measured as the percent of root biomass in poor soil patches)
covaried with 11 of the 53 traits measured and was heavily weighted on the second axis explaining
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trait variation among species (Grime et al. 1997). In a smaller analysis of 16 North American grassland species, Kembel et al. (2008) investigated correlations among a number of plant traits, including foraging precision and the tendency of a plant to acquire growth benefits from heterogeneous
soil (relative to homogeneous soil). They too found that foraging precision was correlated with a
number of functional traits, particularly those associated with a weedy life-history strategy (e.g.,
high leaf nitrogen and growth rates) (Kembel et al. 2008). Interestingly, there is little evidence to
date that foraging precision itself is correlated with plant growth in heterogeneous soil, relative to
growth in homogeneous soil (Kembel & Cahill 2005, Kembel et al. 2008). However, these data
come only from isolated plants under artificial conditions, and growth benefits may only be obvious when plants compete for patches (Hodge et al. 1999, Robinson et al. 1999). Furthermore, such
bivariate correlations do not necessarily reflect the more complex nutrient foraging strategies and
conditions in which such benefits may be expressed. To understand whether nutrient foraging
strategies, rather than single behaviors, better explain plant growth under diverse soil conditions,
one requires methods to quantify and describe multidimensional behavioral strategies.
Methods Used to Understand Correlated Behaviors
Animal ecologists have addressed the need to account for multiple behaviors in several ways, and
application of their methods may help in the study of plants. One approach focuses directly on
correlations and contingencies among behaviors: behavioral syndromes. These can be described as
suites of correlated behaviors displayed by individuals or species that are consistent across multiple
situations (sensu Sih et al. 2004a). Behaviorists can use a direct approach to quantify foraging strategies by measuring different behavioral types within a single individual and can test for correlations
using a phenotypic selection approach (Dingemanse et al. 2002). Although such studies typically are
done within a species, interspecific comparisons are possible (Sih et al. 2004a,b). Such comparisons
would allow researchers to begin to test whether plant foraging strategies, not just individual behaviors, differ among species. This information is critical to ask additional questions regarding the
role of behaviors in mediating species interactions and influencing patterns of species coexistence.
An alternative approach focuses not on the behavior themselves, but on the resulting patterns
of habitat use (e.g., root placement), which are assumed to be the outcome of behavior. Resource
selection functions (RSFs) predict the probability that a particular spatial unit will be used by an
organism based on numerous factors (e.g., resources, temperature, predators, competitors) present
in that spatial unit (Boyce et al. 2002, Lele & Keim 2006, MacKenzie et al. 2002, Manley et al.
2002, McLoughlin et al. 2010). For studies of plant root foraging, data would generally take the
form of the presence/absence of roots. Furthermore, researchers could probably be confident that
plants are foraging within resource units were roots occur. This is different from many animal
studies in which the presence/absence of animals often comes from GPS collars, and presence
does not necessarily indicate that the animal was using the habitat (e.g., the animal may have
simply been walking through). Consequently, plant behaviorists would typically fit a special case
of an RSF called the resource selection probability function (RSPF). The RSPF is estimated by
fitting generalized linear models (GLMs) to binary species presence data as a response variable
and using the various attributes of spatial units as predictors of habitat choice (Boyce et al. 2002,
Lele & Keim 2006, Manley et al. 2002). A GLM fit in this manner generates a linear model that
can predict the probability that a microsite containing a particular combination of conditions will
be used by an organism: the physical outcome of multiple, interacting, behavioral types.
To generate a RSPF, one must measure ecologically important attributes of microsites
throughout the potential range of an organism, as well as the presence/absence of that
individual within each microsite (Lele & Keim 2006). RSPFs can be calculated based on either
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Behavioral
syndrome: suites of
correlated behaviors
displayed by
individuals or species
that are consistent
across multiple
situations
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AN EXAMPLE OF A RESOURCE SELECTION FUNCTION
TO DESCRIBE PLANT FORAGING
Resource selection functions (RSFs) have been used to understand how habitat is used by animals and may provide
insight into plant foraging. For example, Cahill et al. (2010) developed generalized linear models to predict soil
occupancy by the roots Abutilon theophrasti as a function of soil heterogeneity (patch-center, patch-edge, homogeneous), neighbors (presence/absence of neighbors), and distance from the stem (continuous). Although they did not
present the linear model in their paper, the parameter estimates from the model reveal the magnitude and direction
of each main and interaction effect on the probability of finding a plant root at each location in the soil. This
linear model is equivalent to an RSF (see Supplemental Table 1; follow the Supplemental Material link from
the Annual Reviews home page at http://www.annualreviews.org). For example, there was a stronger negative
effect on the probability of finding roots in the presence of neighbors when a nutrient patch was on the edge of
the mesocosm (estimate of −2.51) than when the patch was in the center of the mesocosm between the two plants
(estimate of −0.60), indicating more overlap when the patch was in the center.
Supplemental Material
302
the habitat use of individuals or the aggregated use of multiple individuals within a population.
In either case, for plant roots this will involve collecting root cores, measuring some cues that are
known to influence root placement (e.g., Table 2), and then identifying the presence/absence of
target plant species (or individuals) within that root core (Table 1). Although limited in scope,
one such model has been generated for plant foraging. As described above, A. theophrasti alters
responses to neighbors depending on nutrient heterogeneity (Cahill et al. 2010). This finding
emerged when a GLM was fit to binary data as described above (see the sidebar). Importantly,
predictions derived from RSFs and RSPFs are developed not by building from first principles,
but by modeling observed patterns of habitat use. This is advantageous when there is neither an
a priori expectation of how plants respond to particular soil cues nor any expectation that such
responses would be consistent among species or locations.
We believe that the use of RSFs and RSPFs may be effective for understanding patterns of
the habitat occupancy of individual plant roots and for generating descriptive models of the
whole-plant foraging strategy. A strength of this approach is that the underlying GLMs can
handle many behavioral cues simultaneously, as well as nonadditive interactions (Table 2). A
further benefit of GLMs is that information theoretic approaches can be used to simplify the
linear models and generate testable hypotheses about which factors are likely the most important
under different conditions. RSFs and RSPFs produced from a GLM generate parameter estimates
indicating the magnitude and direction of each cue on the probability of finding a root in a
particular location. This information will be critical to focus research on only the most important
cues that influence root placement in a species, and not on an endless sea of possible soil cues.
Finally, the generation of RSFs and RSPFs for multiple species may facilitate interspecific
comparisons of nutrient foraging strategies.
One of the greatest limitations to the generation of RSFs and RSPFs for understanding the
behavioral determinants of root placement is that it has historically been much easier to track
an elk or wolf with a GPS collar than to identify a root to a species. As a result, behaviorists
have fine-scale and spatially explicit occupancy data for a diversity of animals in remote locations,
whereas similar information for plants under natural conditions is generally lacking. However,
the development of molecular tools (Table 1) should reduce this limitation. A related limitation
is a general lack of small-scale information on microsite conditions, including the distributions
of resources and other organisms. The collection of such information presents technological and
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economic challenges, and until these hurdles are cleared, RSFs related to plant foraging will likely
be limited to only a small number of potential factors. However, animal behaviorists have shown
that when fuller models are generated, there is the potential for great insight into the biology of
the organisms studied (Sih et al. 2004a).
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Linking Behavioral Strategies to Community Dynamics
Interspecific variation in behavioral types and foraging strategies has the potential to influence
community processes in a number of ways. For example, if behavioral types cause altered competitive ability under certain conditions, then this represents one potential assembly rule driving
community structure. Such community-level consequences have been suggested in reference to
interspecific variation in foraging, leading to the idea that competitive outcomes may be contingent on small-scale resource distributions (Bliss et al. 2002, Caldwell et al. 1991b, Campbell et al.
1991, Casper & Cahill 1996, Fransen & de Kroon 2001) and that variation in foraging precision
can influence species coexistence (Campbell et al. 1991). However, it has also been suggested
that plants may simply average out such small-scale variation, and thus it will have no impact on
competitive dynamics and coexistence (Tilman & Pacala 1993). There are not sufficient data to
adequately resolve this issue.
If behavior results in habitat partitioning (i.e., avoidance), then competition among plants may
be reduced, and this may enhance species coexistence (sensu MacArthur 1958). Alternatively, if
behavior results in shared habitat use (i.e., aggregation) among neighboring plants, competition
Outcome of
ecological
interactions
Individual, population,
and community
consequences
Phenotype
(root placement)
Plasticity
Local
conditions
Multiple
cues
Stochasticity
Fixed
development
Genotype
Realized
behavioral
types
Realized root
foraging
strategies
Information
integration
Potential root
foraging
strategies
Potential
behavioral
types
Figure 2
Conceptual model summarizing the main topics presented in this review. In this model, ecological
interactions and larger-scale processes ( purple) are influenced by plant phenotype ( pink), such as patterns of
root placement. The phenotype is determined by three processes (orange), including plasticity. The
expression of plasticity is influenced both by behavioral pathways (blue) and by nonbehavioral responses to
local conditions ( green). Behavioral pathways are themselves influenced by genotype (dark gray), which limits
the potential behaviors that can be expressed (light gray) as well as fixed developmental pathways (orange).
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may be increased, limiting species coexistence. As a result, root placement has as much potential
to influence species coexistence as the details of how different species of warblers feed within trees
(e.g., MacArthur 1958). Unfortunately, the description of patterns of habitat use and microsite
conditions of plant roots is substantially more difficult than the challenges faced by an ornithologist
collecting similar information on birds foraging in the woods. The collection of such microsite
information on habitat use and associated factors for plants remains rare (but see Cahill et al.
2010 and Mommer et al. 2010) and is critical to linking nutrient foraging behavior of plants to
community processes. We suggest that the potential impacts of behavior for plant communities
and plant coexistence are similar to the roles of other functional traits (McGill et al. 2006) but
are understudied. Behavioral strategies need to be more explicitly included in efforts to move
from the individual up to the population or community (Figure 2). Through the quantification of
both developmentally invariable pathways (those commonly associated with functional traits) and
variable pathways (those commonly associated with behavior), there is the possibility of a greater
predictive understanding of how traits influence communities.
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CONCLUSIONS
Above we show that many aspects of the soil environment act as cues, inducing alternative behavioral types in plants. For a given cue, species vary in their behavioral type (Figure 1 and
Table 2), although data are lacking for many factors that influence plant behavior. We also demonstrate that responses to multiple cues are nonadditive and are highly context dependant. Consequently, the resulting behaviors are not always predictable from the behavior of plants faced with
each cue individually (Bliss et al. 2002, Cahill et al. 2010, Hodge et al. 1999, Robinson et al. 1999).
Owing to the contingent nature of plant foraging behavior, we suggest that a multi-dimensional
representation of multiple behaviors defines a plant’s foraging strategy. Drawing from the work of
animal behaviorists, we suggest that behavioral syndromes and resource selection functions may
provide descriptive models of how plants occupy the soil environment. Such models can lead to
an increased understanding of possible plant responses and guide the development of predictive
models. When combined with modern molecular tools for the identification of roots to species,
these statistical tools may lead to the identification of cues and behaviors that most commonly
influence patterns of habitat use among co-occurring individuals.
Differences in habitat use due to variation in behavioral strategies may impact species coexistence. We encourage the integration of foraging strategies with more traditional research on plant
functional traits and resource strategies (Figure 2). This combined approach has the potential to
enhance our understanding of both the functional ecology of plants and the processes that drive
larger-scale processes. There is substantial opportunity for future research in nearly all aspects of
plant foraging behavior, although we highlight several areas in particular need of further research.
FUTURE ISSUES
1. Forde (2009) introduced developmental instability as a concept that involves stochastic
processes shaping root growth and development. Genetic variation in the degree of
instability expressed suggests this stochasticity may have an adaptive role in nutrient
foraging. There is a need to quantify stochasticity for more taxa and determine its role
in plant foraging. The application of the existing theory of animal movement (e.g., Levy
walk; Humphries et al. 2010) to root growth may provide insights into nutrient foraging
strategies.
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2. More theoretical and empirical work is needed to understand how plants respond to
neighbor roots. Few studies have been able to differentiate between behavioral responses
(no response, avoidance, aggregation) and competitive suppression (e.g., allelopathy).
Future studies should include a diversity of taxa, allowing for phylogenetic comparison. Early theoretic work focused primarily on intraspecific competition among annual
plants. Additional theory is needed that accounts for a greater diversity of plants (e.g.,
interspecific competition or perennials) in combination with additional soil factors (e.g.,
predators, nutrient heterogeneity), and this should draw on a diversity of theoretical
approaches.
3. Studies documenting behavioral responses to microbial competitors are nearly nonexistent. Given the ubiquitous nature of plant-microbial interactions in natural systems, this
is a substantial gap in our current understanding. A first step would be to determine what
behavioral types exist, such as no response, avoidance, and aggregation.
4. Similar to microbial competitors, there are few studies or theories describing how root
enemies (e.g., predators and pathogens) alter plant root placement strategies. In contrast, trade-offs between predation risk and foraging reward are among the most classic
examples of contingencies in behavioral type (Lima 1998). We suggest that roots need
to be directly observed through time (e.g., minirhizotron cameras or other methods),
allowing for differentiation between root removals by herbivores (root deaths) compared
with direct behavioral adjustments of root placement (root births).
5. Mycorrhizal fungi draw significant resources from their host plants, and the host plants
draw a variety of resources from the fungi. Viewing this relationship as reciprocal parasitism explicitly recognizes conflicts of interest among the partners, with uncertain
implications for the consequences of differential behavioral types in the host plant.
Furthermore, it is common in animal host-parasite systems for the parasite to manipulate host behavior, typically in a way that benefits the parasite. Whether this is common
in plant-fungal symbiosis is unknown. More data are needed describing whether plants
alter behavior as a function of fungal colonization from a diversity of species. Furthermore, the consequences of differential behavioral types for both the host and the fungi
are completely lacking and are critical to understanding the behavioral ecology of this
interaction.
6. Most studies of plant root foraging have focused on nutrients as a group. This likely
resembles some natural situations in which there can be high spatial covariance among
concentrations of different resources (e.g., urine patch). However, other situations may
result in the elevation of specific nutrients. How plants respond to specific nutrients is
poorly understood, particularly if different resources vary at different locations within a
single root system. Furthermore, despite water being critical for all plants, it has been
greatly understudied in the context of root foraging relative to its biological importance.
7. Animal behaviorists have long lists of behaviors that are expressed in the context of foraging and movement (e.g., ethograms). Plant ecologists do not and instead tend to rely on
root placement or foraging precision as representative of plant foraging behavior. Plants
need to be screened for additional, repeatable aspects of foraging behavior. This needs
to be done with large numbers of taxa, investigating intra- and interspecific variability.
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8. Additional empirical studies are needed that combine multiple environmental cues to
determine how plant behavior is adjusted in the presence of multiple cues. Such data
need to be effectively synthesized into a usable form; resource selection functions may
be a possible tool, although there exist many alternatives in animal behavior and niche
modeling. Theory in this area is also underdeveloped, and concepts such as behavioral
syndromes should be considered.
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9. There are few conceptual or empirical studies that link foraging strategies to larger
patterns of communities and populations. Particularly rare are field studies, and instead
the focus has been on greenhouse and mesocosm experiments. An initial step would be to
develop RSPFs from in situ plant communities, helping to identify consistent cues that
influence root placement. This information can be used to design field experiments.
DISCLOSURE STATEMENT
The authors are not aware of any affiliations, memberships, funding, or financial holdings that
might be perceived as affecting the objectivity of this review.
ACKNOWLEDGMENTS
We thank many colleagues for extensive discussions leading to the ideas presented here, including
Mark Boyce, Hans de Kroon, Liesje Mommer, Colleen St. Clair, Pete Hurd, Joel S. Brown, and
Cam Wild. We thank Pamela Belter, Brenda Casper, Hans de Kroon, Robert Jones, Justine Karst,
Liesje Mommer, Samson Nyanumba, and Rob Jackson for feedback on earlier drafts. This work
was funded by an NSERC Discovery Grant and Accelerator Supplement awarded to J.F.C.
LITERATURE CITED
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Contents
Annual Review of
Ecology, Evolution,
and Systematics
Volume 42, 2011
Native Pollinators in Anthropogenic Habitats
Rachael Winfree, Ignasi Bartomeus, and Daniel P. Cariveau p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 1
Microbially Mediated Plant Functional Traits
Maren L. Friesen, Stephanie S. Porter, Scott C. Stark, Eric J. von Wettberg,
Joel L. Sachs, and Esperanza Martinez-Romero p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p23
Evolution in the Genus Homo
Bernard Wood and Jennifer Baker p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p47
Ehrlich and Raven Revisited: Mechanisms Underlying Codiversification
of Plants and Enemies
Niklas Janz p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p71
An Evolutionary Perspective on Self-Organized Division of Labor
in Social Insects
Ana Duarte, Franz J. Weissing, Ido Pen, and Laurent Keller p p p p p p p p p p p p p p p p p p p p p p p p p p p p91
Evolution of Anopheles gambiae in Relation to Humans and Malaria
Bradley J. White, Frank H. Collins, and Nora J. Besansky p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 111
Mechanisms of Plant Invasions of North America and European Grasslands
T.R. Seastedt and Petr Pyšek p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 133
Physiological Correlates of Geographic Range in Animals
Francisco Bozinovic, Piero Calosi, and John I. Spicer p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 155
Ecological Lessons from Free-Air CO2 Enrichment (FACE) Experiments
Richard J. Norby and Donald R. Zak p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 181
Biogeography of the Indo-Australian Archipelago
David J. Lohman, Mark de Bruyn, Timothy Page, Kristina von Rintelen,
Robert Hall, Peter K.L. Ng, Hsi-Te Shih, Gary R. Carvalho,
and Thomas von Rintelen p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 205
Phylogenetic Insights on Evolutionary Novelties in Lizards
and Snakes: Sex, Birth, Bodies, Niches, and Venom
Jack W. Sites Jr, Tod W. Reeder, and John J. Wiens p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 227
v
ES42-FrontMatter
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16:5
The Patterns and Causes of Variation in Plant Nucleotide Substitution Rates
Brandon Gaut, Liang Yang, Shohei Takuno, and Luis E. Eguiarte p p p p p p p p p p p p p p p p p p p p p 245
Long-Term Ecological Records and Their Relevance to Climate Change
Predictions for a Warmer World
K.J. Willis and G.M. MacDonald p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 267
The Behavioral Ecology of Nutrient Foraging by Plants
James F. Cahill Jr and Gordon G. McNickle p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 289
Climate Relicts: Past, Present, Future
Arndt Hampe and Alistair S. Jump p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 313
Annu. Rev. Ecol. Evol. Syst. 2011.42:289-311. Downloaded from www.annualreviews.org
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Rapid Evolutionary Change and the Coexistence of Species
Richard A. Lankau p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 335
Developmental Patterns in Mesozoic Evolution of Mammal Ears
Zhe-Xi Luo p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 355
Integrated Land-Sea Conservation Planning: The Missing Links
Jorge G. Álvarez-Romero, Robert L. Pressey, Natalie C. Ban, Ken Vance-Borland,
Chuck Willer, Carissa Joy Klein, and Steven D. Gaines p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 381
On the Use of Stable Isotopes in Trophic Ecology
William J. Boecklen, Christopher T. Yarnes, Bethany A. Cook, and Avis C. James p p p p 411
Phylogenetic Methods in Biogeography
Fredrik Ronquist and Isabel Sanmartı́n p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 441
Toward an Era of Restoration in Ecology: Successes, Failures,
and Opportunities Ahead
Katharine N. Suding p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 465
Functional Ecology of Free-Living Nitrogen Fixation:
A Contemporary Perspective
Sasha C. Reed, Cory C. Cleveland, and Alan R. Townsend p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 489
Indexes
Cumulative Index of Contributing Authors, Volumes 38–42 p p p p p p p p p p p p p p p p p p p p p p p p p p p 513
Cumulative Index of Chapter Titles, Volumes 38–42 p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 517
Errata
An online log of corrections to Annual Review of Ecology, Evolution, and Systematics
articles may be found at http://ecolsys.annualreviews.org/errata.shtml
vi
Contents