The Behavioral Ecology of Nutrient Foraging by Plants · The Behavioral Ecology of ... that forms of questions for any behavior could be classiﬁed as: ... A review on this subject

ES42CH14-Cahill ARI 7 October 2011 15:40

The Behavioral Ecology ofNutrient Foraging by PlantsJames 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

First published online as a Review in Advance onAugust 29, 2011

The Annual Review of Ecology, Evolution, andSystematics is online at ecolsys.annualreviews.org

This article’s doi:10.1146/annurev-ecolsys-102710-145006

Copyright c© 2011 by Annual Reviews.All rights reserved

1543-592X/11/1201-0289$20.00

∗Current address: Department of BiologicalSciences, University of Illinois, Chicago,Illinois 60607; email: [email protected].

Keywords

root ecology, plant behavior, plant-soil interactions, plant strategies,precision, resource selection functions

Abstract

Foraging for resources influences ecological interactions among individualsand species, regardless of taxonomic affiliation. Here we review studies ofnutrient foraging in plants, with an emphasis on how nutritious and non-nutritious cues in the soil alter behavioral decisions and patterns of rootplacement. Three patterns emerge: (a) Plants alter root placement in re-sponse to many diverse cues; (b) species respond differently to these cues;and (c) there are nonadditive responses to multiple cues, indicating that plantsexhibit complex multidimensional root foraging strategies. We suggest thatthis complexity calls for novel approaches to understanding nutrient forag-ing by plants. Resource selection functions are commonly used by animalbehaviorists and may be useful to describe plant foraging strategies. Under-standing such approaches may allow researchers to link individual behaviorto population and community dynamics.

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Behavior: weuse Silvertown &Gordon’s (1989)definition of behavior,“what a plant or animaldoes, in the course ofan individual’slifetime, in response tosome event or changein its environment”

Foraging: theact of searching for,acquiring, andconsuming resources

Proximate andultimate: theethologist NikolaasTinbergen suggestedthat forms of questionsfor any behavior couldbe classified as: theproximate (i.e.,evolutionary)mechanisms ofadaptation andevolution

Cue: a specific signalthat can induce abehavioral response

Behavioral type:within a genotype orspecies, a consistentbehavioral response toa specific cue

INTRODUCTION

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 forsoil nutrients. Finding and acquiring resources are fundamental aspects of the ecology of organ-isms, and behaviorists have studied both proximate and ultimate (sensu Tinbergen 1963) aspectsof foraging ecology (Stephens et al. 2007). Behaviorists recognize that individual decisions areinfluenced by genetic constraints as well as multiple local cues (e.g., resources, predators). Herewe define the combined responses to multiple cues as an organism’s foraging strategy. Variationin foraging strategies among individuals and species can have consequences for population andcommunity dynamics. To facilitate understanding, we provide a critical review of these behavioralaspects of plant foraging for nutrients.

Plant root growth is plastic, resulting in nonuniform root distributions in soil. Some of thisplasticity results from the plant’s condition (e.g., size, health), whereas some results from soilfactors such as resource distributions or neighbor presence (Hodge 2004, Hutchings & de Kroon1994, Kembel & Cahill 2005, Robinson 1996). Because resource capture influences the energeticand nutritive state of organisms, behaviorists recognize the importance of understanding foragingecology (Stephens et al. 2007) in order to understand broader aspects of an organism’s life historyand 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 biomassin many communities ( Jackson et al. 1996), and a significant proportion of photosynthate is fedto fungi and bacteria that live in and around plant roots (Wardle 2002). Furthermore, compe-tition 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 influencedby the spatial distribution of roots among co-occurring plants. However, processes that determinewhere plants put their roots within the soil are poorly understood in comparison with analogousaboveground processes in plants (de Kroon & Hutchings 1995) and movement patterns amongvertebrates.

Here we review the behavioral ecology of plant root foraging for nutrients, linking local de-cisions to larger-scale impacts. We focus primarily on the behavioral aspects of root growth andplacement, although many other aspects of nutrient foraging are also important (e.g., uptake ki-netics). 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 inour understanding of plant foraging behavior. Here we provide a critical assessment of currentknowledge 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 elkfeeds as a function of resource distributions. There exists substantial interspecific variation in rootplacement relative to cues, suggesting adaptive benefits of different behavioral responses underdifferent conditions (see below). Building on the terminology of Sih (2004b), we refer to consistentdifferences 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 inresponse 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 representativereferences

Method Description Advantages Disadvantages Reference(s)Excavation Whole root systems or plots

are excavated by diggingGenerates largeamounts ofhigh-quality data;provides identificationto the level of theindividual and species

Often misses fine rootsdestroyed by digging;extremely laborious andimpractical for experimentswith many treatments orsubstantial replication

Brisson & Reynolds1994

Root chambers,rhizotrons,andminirhizotrons

Roots are observed/photographed throughclear material sunk in theground, in rootingchambers, or throughtubes buried in soil

Adaptable togreenhouse and fieldstudies; can beinexpensive

Measures a tiny fraction ofthe root volume, in twodimensions; hard to identifyroots to the individual orspecies; time-consumingimage analysis

Fransen & de Kroon2001, McNickle &Cahill 2009,Mommer et al. 2010

Staining Colored dye is injected intothe root system of focalplants, with identificationbased on color; can beused in combination withimaging

Inexpensive Difficult to get reliable andeven staining of roots; laborintensive

Cahill et al. 2010,Schenk et al. 1999

Fluorescenceand reflectance

Roots are exposed todifferent wavelengths oflight; differentialfluorescence (ultraviolet)or reflectance indicatesspecies identity

Can allow fordetermination ofrelative abundance;once equipmentpurchased,inexpensive

Poorly understood andhighly species/systemspecific; time-consumingimage analysis

Caldwell et al. 1991a,Roumet et al. 2006

Tracers Rare elements orcompounds are injectedinto soil; shoots areanalyzed, and thepositioning of roots isinferred from elevatedshoot concentrations

Can provideidentification to thelevel of the individualor species; relativelysimple

Low number of tracersrestrict the number of soillocations measured; doesnot differentiate betweenroot and fungal uptake;chemical analyses arerelatively expensive

Casper et al. 2000

Morphologicalidentification

Roots are collected andidentified based onmorphological differences

Can provideidentification to thelevel of species;inexpensive

Intraspecific variation in rootmorphology limitsinterspecific comparisons;identification of individualsis not possible; time-consuming

Gasson 1979

Molecularidentification

Species are determinedfrom root tissue on thebasis of DNA markers

Can provideidentification to thelevel of individual orspecies; increasinglyinexpensive

Requires expertise withmolecular techniques;high-throughput methodsstill being developed;typically results inpresence/absence data, notrelative abundance

Bobowski et al. 1999,Brunner et al. 2001,Jackson et al. 1999,McNickle et al.2008, Moore &Field 2005, Ridgwayet al. 2003, Taggartet al. 2010

(Continued )

www.annualreviews.org • Plant Foraging Behavior 291

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Table 1 (Continued )

Method Description Advantages Disadvantages Reference(s)Quantitativepolymerasechain reaction(PCR)

Similar to molecularidentification, exceptquantitative real-timePCR is used to identifyspecies and estimaterelative abundance

Can providespecies-specificabundance anddistribution data fromsamples

Requires a reliable internalstandard to obtain usefulquantitative PCR data;limited to a low number ofspecies per sample;expensive

Mommer et al. 2008,2010

Resource selectionfunction (RSF): anystatistical model thatgives the probabilitythat a unit of habitatwill be used by anorganism based onbiotic or abioticvariables associatedwith each habitat unit

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 wediscuss integrative nutrient foraging strategies. We introduce resource selection functions (RSFs),a statistical approach often used to model habitat selection and describe patterns of occupancyby foraging animals, as one potential tool to describe root placement in response to multiplecues. Such information is critical to inferring and defining alternative nutrient foraging strategiesamong individuals and species.

Finally, we present a conceptual overview of how plant foraging for nutrients can be integratedinto existing frameworks linking plant traits to larger-scale processes. We also highlight specificareas we believe are the highest priorities for additional research in the general area of plantforaging.

WHAT SOIL FACTORS INFLUENCE WHERE AN INDIVIDUALPLANT PLACES ITS ROOTS IN THE SOIL?

Since the work of Drew (1975) and Drew et al. (1973), it has become apparent that nutrients serveas cues influencing root growth and placement. Consequently, root system architecture is highlydynamic, 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 biotaand neighboring plants (e.g., Cahill et al. 2010, Stevens & Jones 2006). Combined, this evidencesuggests that plants exhibit fine-scale variation in lateral and vertical root distributions, drivenin large part by behavioral response to diverse soil cues. In this section we review the differentcues that may impact root placement in soil and the alternative behavioral types that exist amongspecies. However, we begin by emphasizing that not all variation in root growth and placement isnecessarily the outcome of behavior.

Stochastic Influences on Root Development

Genetically identical plants grown under environmentally uniform conditions do not necessar-ily produce identical patterns of root placement; instead they may exhibit stochasticity in rootplacement (reviewed in Forde 2009). This random variation in root development has been calleddevelopmental instability (Forde 2009), and its effects on root architecture can be substantial, evensurpassing variation induced by environmental cues (Forde 2009).

The degree to which a plant’s root system architecture is influenced by stochastic processes hasa genetic component, suggesting that natural selection may shape developmental instability (Forde2009). The potential benefits of random processes as part of a nutrient foraging strategy are notknown, although it may reduce intraplant root competition and increase the probability of findinglocalized 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 degreesof stochasticity in searching (Smouse et al. 2010). Analogous research linking root placement andstochastic search processes is lacking. Differentiating among stochastic and behavioral causes ofroot placement patterns remains a challenge.

Root Responses to Nutritious Cues

The spatial distribution of soil nutrients varies at scales smaller ( Jackson & Caldwell 1993) andlarger than the size of a single plant. Here we focus on variation at scales within the soil potentiallyexplored by an individual plant, as this is most relevant for behavioral decisions. The effects ofresource 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 & Pacala1993), and thus we focus on a behavioral interpretation of these responses.

When nutrients are heterogeneously distributed in soil, plants generally place more rootsinto locations with higher nutrient concentrations (Hodge 2004, Kembel & Cahill 2005). Thisbehavioral 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, ratherthan a developmental response. For example, foraging precision can be expressed developmentallyeither through increased root growth (e.g., Bilbrough & Caldwell 1995) or through decreased rootmortality (Gross et al. 1993), but the resulting behavior is identical.

Although most species alter root placement in response to nutritious cues (Kembel & Cahill2005), there is substantial interspecific variation (Figure 1). Some species place a large proportionof 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), display-ing the behavioral type of low precision. Some variation in behavioral type is phylogenetically

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.30 20 40 60 80 100 120

Eudicots

Monocots

Rank

Precision

Figure 1Rank order of 120 species’ foraging precision measured as the proportion of fine roots in nutrient-enriched patches (0.5 = randomroot growth with respect to nutrient distributions; 1.0 = all roots are located in nutrient-enriched patches). Monocots are indicated bydark 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 demo-graphy (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 behavioraldifferences 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. Existingstudies tend to either manipulate resources as a group by adding NPK or organic fertilizers, orthey tend to manipulate a single nutrient, without comparing responses to other nutrients. Suchtendencies may reflect the chemistry of natural nutrient patches (e.g., urine patches) and yieldthe most useful ecological information, but they also limit the ability to draw conclusions aboutpotential behavioral responses to specific compounds.

In addition to changes in root placement, nutritious cues can influence the transient processesof root growth. For example, consistent with a prediction of the marginal value theorem (Charnov1976), the presence of nutrient-rich patches can cause plants to reduce their rate of soil explorationsuch 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 (Grosset al. 1993), also resulting in a pattern of increased residency in nutrient-rich patches relativeto nutrient-poor patches. Few species have been screened for the transient processes describedhere, and as a result, the generality of these behaviors and potential alternative behavioral typesare unknown.

At the individual level, it is critical to know which specific traits directly influence resourcecapture and may be subject to optimization. Several cost-benefit approaches to understandingroot growth have been proposed, including a focus on the resource capture efficiency of individualroots (Eissenstat et al. 2000) and whole-plant fitness as a function of alternative allocations toindividual nutrient foraging strategies (McNickle et al. 2009). However, data explicitly testingthese possibilities are lacking. Instead, there has been a general assumption that root biomassdistributions are reflective of functionally important behavioral decisions. This assumption hasgenerally not been challenged with data.

Molecular Mechanisms of Root Placement

Most of our understanding of the genes that govern root placement comes primarily from studiesof Arabidopsis thaliana. As a result, the molecular basis to explain the breadth of alternative behav-ioral types known to exist (Figure 1) is lacking. Root placement can be influenced by root-levelresponses to the local conditions and, under some circumstances, systemic modulation of the re-sponse 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 influenceboth root growth rate and the production of secondary lateral roots have been described (Chenet 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) andinvolves 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 (Chenet 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 expectedthat root distributions reflect not only resource distributions but also the distributions and pres-sures 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.

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 areproduced (Novoplansky 2009). Litav & Harper (1967) suggested three ways that competition mayinfluence 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- andheterospecific neighbors. Building on the concepts of Litav & Harper (1967), we suggest thereare 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 (relativeto shoot size) regardless of the presence or identity of neighboring roots. Importantly, such apattern can occur either because a plant is unable to detect/respond to neighbors or becauseplants do respond to neighboring roots but do not differentiate between their roots and those ofdifferent 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). Thisbehavioral 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 noresponse 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 typeexpressed by an organism in response to one cue is often contingent on the presence or absenceof 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 for-malized for annual plants with simple game theoretic models that assume plants compete only forresources by increasing root biomass. The resulting evolutionary stable strategy is the behavioraltype of aggregation, even when this increased root production comes at the cost of root growth inunoccupied 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 intheir experimental designs, resulting in sharp criticism (Hess & de Kroon 2007, Laird & Aarssen2005, Schenk 2006). Although this critique has been contested (O’Brien & Brown 2008), data arelimited. However, if the behavioral type of aggregation occurs, overmixing of roots should occurin locations of root overlap among neighboring plants. Depending on compensatory responseselsewhere in the root system, overmixing, undermixing, or random root distributions could befound at the whole-plot level.

The third behavioral type expressed in response to neighbor roots is avoidance, which resultsin the undermixing (i.e., segregation) of roots. Schenk et al. (1999) identified 13 species of plantsfrom 16 studies that exhibited some degree of root segregation, with additional examples foundin more recent studies (e.g., Cahill et al. 2010, Gersani et al. 1998, Holzapfel & Alpert 2003, vonFelten & Schmid 2008). However, undermixing can occur both through avoidance and through thesuppression 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 foragingbehaviors in plantsa

Effect on root placement Species studied Reference(s)Developmentalinstability

Genetically identical plants mayplace roots differently owing todevelopmental instability

Arabidopsis thaliana, Zea mays Reviewed in Forde 2009

Resources Plants generally preferentiallyplace roots into nutrient-richpatches

>120 species (see Kembel & Cahill2005 for an extensive list)

Reviewed in Hodge 2004, 2006,2009; Hutchings & de Kroon1994; Kembel & Cahill 2005;Robinson 1996

Plant neighbors No response: Plants do not adjustroot placement in response to aneighbor

Pisum sativum, Avena sativa(N-poor soil only), Leucanthemumvulgare, Plantago lanceolata

Litav & Harper 1967, Mommeret al. 2010

Aggregation: Plants increase rootgrowth near a neighbor; oftencalled overproliferation

Glycine max, Phaseolus varigaris,P. sativum, Anthoxanthumodoratum, Festuca rubra

Gersani et al. 2001, Maina et al.2002, Mommer et al. 2010,O’Brien et al. 2005

Avoidance: Plants reduce growthnear a neighbor; one of severalmechanisms leading to rootsegregation

Pisum sativum, A. sativa (N-richsoil only), Cakile edentula, Abutilontheophrasti, and 13 more speciesfrom 19 papers reviewed inSchenk et al. (1999)

Cahill et al. 2010, Dudley & File2007, Gersani et al. 1998, Litav& Harper 1967, Schenk et al.1999

Microbe neighbors Too few dataOther mutualists Too few dataSoil enemies Too few data

aTable limited to studies cited in the main text.

These are fundamentally different responses and highlight that the description of root patterns insoils 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 com-mon behavioral type in response to neighbor cues (Table 2). However, we caution that it is usuallynot clear in these studies whether avoidance is caused by behavioral shifts of one plant in responseto another or whether it is an outcome of chemically mediated interference competition. Fur-thermore, the literature also shows that all three behavioral types do exist and that an individualspecies may express multiple behavioral types depending on other cues.

Unfortunately, although many studies document patterns of root placement among co-occurring plants (e.g., Table 2), few studies directly observe changes in the root growth of oneplant in response to roots of another (e.g., Cahill et al. 2010, Mahall & Callaway 1991, Mommeret al. 2010). Such studies are logistically challenging but are critical to draw conclusions about thespecific behaviors plants express, rather than simply describing the pattern of root distributionsthat are the outcome of behavioral changes. We anticipate that recently developed molecularmethods 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 ofthe potential competitor. For example, simulations of root system development typically includeparameters that minimize the potential for overlap among intraplant (complete genetic similar-ity) 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 dependenton whether the neighbors are part of the same connected genet or whether connections have beensevered (Falik et al. 2003, Gruntman & Novoplansky 2004).

There is also evidence for kin recognition by plant roots (Dudley & File 2007), suggesting thata plant could enhance inclusive fitness by minimizing competition with close relatives (Hamilton1964a,b). For example, Dudley & File (2007) found that Cakile edentula reduced competition withkin through the production of less fine root biomass compared with when plants competed withunrelated individuals. Similar results have been found for Impatiens pallida (Murphy & Dudley2009). More studies are needed to understand the generality of these responses.

Root Placement Responses to Microbial Competitors

Plants compete with fungi and bacteria for soil nutrients (e.g., Schimel & Chapin 1996). Further-more, microbial community composition can demonstrate substantial variation at scales smallerthan an individual root system (Franklin & Mills 2003), suggesting that the effects of plant-microbial interactions also vary at small spatial scales (Hodge et al. 2000a). Thus we would expectthat microbial distributions would be another cue influencing root placement, perhaps leading tobehavioral 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 whichplant-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 commonamong plants. These associations are generally viewed as mutualisms yielding nutrient benefitsto the plant at the expense of carbon to feed the microbes (Smith & Read 1997). However, theseinteractions can vary from parasitism to mutualism as a function of nutrient conditions ( Johnsonet al. 1997). Consequently, there is reason to expect that mycorrhizal interactions will alter aspectsof plant nutrient foraging behavior.

A handful of studies have measured whether mycorrhizae alter a plant’s foraging preci-sion in response to nutritious cues. For example, Hodge (2001) grew Plantago lanceolata in soilwith heterogeneous soil nutrients, both with and without arbuscular mycorrhizal fungi (AMF).Although plants increased root births when AMF were present, foraging precision (as measured asroot length inside a nutrient patch) was unaffected. Similarly, Wijesinghe et al. (2001) found thatnone 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 andmycorrhizal status, particularly for aspects of foraging other than precision.

Even if foraging precision is unaltered by mycorrhizal colonization, plants infected by myco-rrhizal 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 conventionalmeasures of nutrient foraging (e.g., biomass allocation) are influenced by mycorrhizal status, evenif other aspects of nutrient foraging (e.g., precision) are not.

Mycorrhizal associations are often described as mutualisms that are less costly to plants thanthe production of fine roots (Fitter 1991) and in which fungal hyphae serve as simple extensions ofplant roots (Cui & Caldwell 1996, Tibbett 2000). However, mycorrhizal fungi are not simply rootextensions, 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 reciprocalparasitism (e.g., Egger & Hibbett 2004) rather than root extensions may result in novel insightinto plant foraging behavior. Under this model, there is explicit recognition of conflicts of interestamong the partners engaged in a potential mutualism and that selection acts on individuals, andnot on the interaction as a whole. Adapting this view may help in the identification of similarities inbehavioral responses to putative mutualists, as well as traditional root parasites (described below).

Root Placement Responses to Enemies

Root enemies consist of herbivores and pathogens/parasites from a variety of taxa and have thepotential for direct and indirect effects on root growth (Wardle 2002). Differential patterns ofroot placement can occur (a) if root herbivores show preference for which roots they consumeor (b) if plants alter root growth as a function of the distribution of enemies in the soil. Asroot enemies feed, they can consume roots and reduce the local abundance of roots, alteringroot distributions (Stevens & Jones 2006, Stevens et al. 2007). For example, if root herbivoresdemonstrate a numerical response to root densities (e.g., distribute according to an ideal freedistribution), their feeding should homogenize root distributions within the soil. However, it willbe difficult to separate the effects of plants altering root placement from the effects of soil enemiesconsuming roots, thereby altering locations that contain roots. How plants modify root placementin response to soil enemies is poorly understood.

Plants can detect belowground enemies, although what plants do with that information isnot always clear. For example, Zea mays is capable of detecting the root herbivore Diabroticavirgifera virgifera, responding by releasing (E)-β-caryophyllene and thereby attracting predatorynematodes (Rasmann et al. 2005). However, critical to understanding behavioral responses tosoil enemies are data documenting the dynamics of root growth. Unfortunately, most studiesinvolving soil enemies (Table 2) focus on total root biomass at the end of the experiment, apattern caused either by behavioral decisions of the plant or by consumption of the plant by theherbivore/pathogen. Once again, we suggest that measures of the transient aspects of root growthand placement are needed to understand the behavior of plant root foraging.

HOW DO ROOT DISTRIBUTION RESPONSESTO MULTIPLE FACTORS COMBINE TO DESCRIBEA 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 speciesare variable in the behavioral types they exhibit (Figure 1 and Table 2). However, behavioralresponses to multiple environmental cues can be nonadditive, generating complex multidimen-sional behavioral strategies. Consequently, the population- or community-level consequences ofa 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) havereceived limited attention. Consequently, the effects of multiple, simultaneous cues on root place-ment are generally difficult to predict. However, we can draw from the concepts of trade-offs andconstraints to provide a context in which to begin to understand the available information. At a basiclevel, if a plant’s response to multiple cues were additive, we would expect to find consistent effectsof 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 experi-mental manipulations of resource distributions and neighbors (Hutchings et al. 2003). If plantresponses to nutritious cues are constant, soil nutrient heterogeneity might be expected to haveconsistent additive effects on larger-scale measures, such as population biomass, regardless ofthe levels of other factors (e.g., density of neighboring plants). Such consistency is not appar-ent, indicating context dependency on the expression of behavioral type (i.e., statistical interac-tion). 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 (intermediate-sized nutrient patches, plants grown for a short duration), population growth in heterogeneoussoil was 33% less than in homogeneous soils. Under another set of conditions (low nutrients, largepatches), 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, nutrientheterogeneity reduced population biomass 17% relative to homogeneous controls under one setof conditions (intermediate density, small and infrequent patches) but increased productivity by44% under another combination of conditions (intermediate density, small and frequent patches)(Casper & Cahill 1996, 1998). Clearly, nutrient heterogeneity may have nonadditive effects onpopulation growth, even within a single species.

Underlying these varied responses of populations to soil nutrient heterogeneity are changesin individual plant responses to soil cues, and these individual responses are also nonadditive. Forexample, Cahill et al. (2010), also working with Abutilon theophrasti, combined root staining withimages from a minirhizotron camera to directly observe root distributions in relation to both thelocation of soil patches and the presence or absence of neighbors. Under homogeneous nutrientconditions, this species exhibited strong root avoidance. However, soil nutrient heterogeneity re-duced the magnitude of root avoidance, resulting in the overlapping of neighboring roots withinnutrient-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 heteroge-neous than homogenous conditions (Casper et al. 2003). If root distributions alter individual-and population-level resource uptake and growth, these shifts in behavioral responses to soil cueslikely 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-nutritiouscues. For example, Semchenko et al. (2007) observed the altered root placement of two species inresponse 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 Fragariavesca roots were stimulated by inter- but not intraspecific neighbors. There is also evidence thatthe benefits of mycorrhizal colonization can be most strongly expressed when plants competefor 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 multiplesoil cues appears to be common. This indicates a need for a multi-cue approach to understandplant 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 fora foundation on which to rest the contrasting results. However, the study of plant behaviorhas been primarily driven by empiricists rather than theoreticians. This has resulted in a sit-uation in which the theoretical aspects of plant behavior are relatively underdeveloped both


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Root foragingstrategy: thecombination ofbehavioral typesexpressed by anindividual plant inresponse to multiplecues related to rootforaging

in comparison with that of animal behavior and in comparison with the number of existingexperiments.

The most extensive work in plant behavioral theory relates to clonal plants and the optimalplacement of daughter plants in a variable environment (de Kroon & Hutchings 1995). For exam-ple, using simulation modeling, Sutherland & Stillman (1988) described how soil conditions shouldfavor particular behavioral types, such as reducing internodal length in areas of high-resource avail-ability. Simulation modeling of nonclonal plants has also been used to show potential benefits ofroot responses to localized nutrient patches ( Jackson & Caldwell 1996, Ryel & Caldwell 1998).

Similarly, de Kroon & Hutchings (1995) recognized the linkages between plant modularityand the fine-scale placement of roots and emphasized the need to develop a foraging theory thatcould apply to all plants. The identification of plant modularity as critical to understanding how aplant interacts with its environment is old (White 1979); however, the importance of this insightis 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 predic-tions about root placement in soil. For example, root placement in response to nutritious cueshas been placed in the theoretical framework of optimal patch-use models, such as the marginalvalue 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 distribu-tions (Gersani et al. 1998). An alternative to game-theoretic approaches is that of swarming, withindividual root tips acting as individuals (Baluska et al. 2010).

Despite these examples, there have been few attempts to generate theory about how multiplebehaviors may interact to influence whole-plant foraging strategies, individual fitness, and con-sequences at the population and community levels. Instead, we are left with a (small) numberof theoretical works devoted to single behavioral types, on one hand, and a (small) number ofempirical studies that demonstrate that the behavioral type expressed by a plant can be contingenton multiple cues in the soil, on the other hand. We suggest that a critical missing piece in theliterature is a conceptual understanding of how individual behaviors combine to form behavioralstrategies, along with the related plant-specific theory.

Developing Foraging Strategies

Here we define a plant’s nutrient foraging strategy as the combination of behavioral types expressedby 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, aggrega-tion) defines a second behavioral type (Table 2). That individual’s foraging strategy is defined bythe combination of these behavioral types (e.g., avoidance + high precision), along with behav-ioral types induced by other cues (e.g., mutualists, enemies) and any contingencies in behavioralexpression (e.g., avoidance only in homogeneous soils).

Although there have yet to be explicit efforts to describe a plant’s overall nutrient foragingstrategy, the idea that nutrient foraging behaviors should be integrated into a general understand-ing 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 life-history 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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Behavioralsyndrome: suites ofcorrelated behaviorsdisplayed byindividuals or speciesthat are consistentacross multiplesituations

trait variation among species (Grime et al. 1997). In a smaller analysis of 16 North American grass-land species, Kembel et al. (2008) investigated correlations among a number of plant traits, includ-ing foraging precision and the tendency of a plant to acquire growth benefits from heterogeneoussoil (relative to homogeneous soil). They too found that foraging precision was correlated with anumber 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 todate that foraging precision itself is correlated with plant growth in heterogeneous soil, relative togrowth in homogeneous soil (Kembel & Cahill 2005, Kembel et al. 2008). However, these datacome only from isolated plants under artificial conditions, and growth benefits may only be obvi-ous when plants compete for patches (Hodge et al. 1999, Robinson et al. 1999). Furthermore, suchbivariate correlations do not necessarily reflect the more complex nutrient foraging strategies andconditions in which such benefits may be expressed. To understand whether nutrient foragingstrategies, 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, andapplication of their methods may help in the study of plants. One approach focuses directly oncorrelations and contingencies among behaviors: behavioral syndromes. These can be described assuites of correlated behaviors displayed by individuals or species that are consistent across multiplesituations (sensu Sih et al. 2004a). Behaviorists can use a direct approach to quantify foraging strate-gies by measuring different behavioral types within a single individual and can test for correlationsusing a phenotypic selection approach (Dingemanse et al. 2002). Although such studies typically aredone within a species, interspecific comparisons are possible (Sih et al. 2004a,b). Such comparisonswould allow researchers to begin to test whether plant foraging strategies, not just individual be-haviors, differ among species. This information is critical to ask additional questions regarding therole 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 patternsof habitat use (e.g., root placement), which are assumed to be the outcome of behavior. Resourceselection functions (RSFs) predict the probability that a particular spatial unit will be used by anorganism based on numerous factors (e.g., resources, temperature, predators, competitors) presentin 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 theform of the presence/absence of roots. Furthermore, researchers could probably be confident thatplants are foraging within resource units were roots occur. This is different from many animalstudies in which the presence/absence of animals often comes from GPS collars, and presencedoes not necessarily indicate that the animal was using the habitat (e.g., the animal may havesimply been walking through). Consequently, plant behaviorists would typically fit a special caseof an RSF called the resource selection probability function (RSPF). The RSPF is estimated byfitting generalized linear models (GLMs) to binary species presence data as a response variableand 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 thatcan predict the probability that a microsite containing a particular combination of conditions willbe used by an organism: the physical outcome of multiple, interacting, behavioral types.

To generate a RSPF, one must measure ecologically important attributes of micrositesthroughout the potential range of an organism, as well as the presence/absence of thatindividual within each microsite (Lele & Keim 2006). RSPFs can be calculated based on either


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AN EXAMPLE OF A RESOURCE SELECTION FUNCTIONTO DESCRIBE PLANT FORAGING

Resource selection functions (RSFs) have been used to understand how habitat is used by animals and may provideinsight into plant foraging. For example, Cahill et al. (2010) developed generalized linear models to predict soiloccupancy by the roots Abutilon theophrasti as a function of soil heterogeneity (patch-center, patch-edge, homoge-neous), neighbors (presence/absence of neighbors), and distance from the stem (continuous). Although they did notpresent the linear model in their paper, the parameter estimates from the model reveal the magnitude and directionof each main and interaction effect on the probability of finding a plant root at each location in the soil. Thislinear model is equivalent to an RSF (see Supplemental Table 1; follow the Supplemental Material link fromthe Annual Reviews home page at http://www.annualreviews.org). For example, there was a stronger negativeeffect on the probability of finding roots in the presence of neighbors when a nutrient patch was on the edge ofthe 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.

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 areknown to influence root placement (e.g., Table 2), and then identifying the presence/absence oftarget 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 altersresponses to neighbors depending on nutrient heterogeneity (Cahill et al. 2010). This findingemerged 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 ana priori expectation of how plants respond to particular soil cues nor any expectation that suchresponses would be consistent among species or locations.

We believe that the use of RSFs and RSPFs may be effective for understanding patterns ofthe habitat occupancy of individual plant roots and for generating descriptive models of thewhole-plant foraging strategy. A strength of this approach is that the underlying GLMs canhandle many behavioral cues simultaneously, as well as nonadditive interactions (Table 2). Afurther benefit of GLMs is that information theoretic approaches can be used to simplify thelinear models and generate testable hypotheses about which factors are likely the most importantunder different conditions. RSFs and RSPFs produced from a GLM generate parameter estimatesindicating the magnitude and direction of each cue on the probability of finding a root in aparticular location. This information will be critical to focus research on only the most importantcues 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 interspecificcomparisons of nutrient foraging strategies.

One of the greatest limitations to the generation of RSFs and RSPFs for understanding thebehavioral determinants of root placement is that it has historically been much easier to trackan elk or wolf with a GPS collar than to identify a root to a species. As a result, behavioristshave 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 limitationis a general lack of small-scale information on microsite conditions, including the distributionsof resources and other organisms. The collection of such information presents technological and


Supplemental Material

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http://www.annualreviews.org/doi/suppl/10.1146/annurev-ecolsys-102710-145006

http://www.annualreviews.org/doi/suppl/10.1146/annurev-ecolsys-102710-145006


economic challenges, and until these hurdles are cleared, RSFs related to plant foraging will likelybe limited to only a small number of potential factors. However, animal behaviorists have shownthat when fuller models are generated, there is the potential for great insight into the biology ofthe organisms studied (Sih et al. 2004a).

Linking Behavioral Strategies to Community Dynamics

Interspecific variation in behavioral types and foraging strategies has the potential to influencecommunity processes in a number of ways. For example, if behavioral types cause altered com-petitive ability under certain conditions, then this represents one potential assembly rule drivingcommunity structure. Such community-level consequences have been suggested in reference tointerspecific variation in foraging, leading to the idea that competitive outcomes may be contin-gent 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 precisioncan influence species coexistence (Campbell et al. 1991). However, it has also been suggestedthat plants may simply average out such small-scale variation, and thus it will have no impact oncompetitive dynamics and coexistence (Tilman & Pacala 1993). There are not sufficient data toadequately resolve this issue.

If behavior results in habitat partitioning (i.e., avoidance), then competition among plants maybe reduced, and this may enhance species coexistence (sensu MacArthur 1958). Alternatively, ifbehavior results in shared habitat use (i.e., aggregation) among neighboring plants, competition

GenotypeLocalconditions

Multiplecues

Realizedbehavioral

types

Realized rootforaging

strategies

Potential rootforaging

strategies

Potentialbehavioral

types

Informationintegration

Outcome ofecological

interactions

Individual, population,and communityconsequences

Phenotype(root placement)

FixeddevelopmentPlasticity Stochasticity

Figure 2Conceptual model summarizing the main topics presented in this review. In this model, ecologicalinteractions and larger-scale processes ( purple) are influenced by plant phenotype ( pink), such as patterns ofroot placement. The phenotype is determined by three processes (orange), including plasticity. Theexpression of plasticity is influenced both by behavioral pathways (blue) and by nonbehavioral responses tolocal conditions ( green). Behavioral pathways are themselves influenced by genotype (dark gray), which limitsthe 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 potentialto 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 micrositeconditions of plant roots is substantially more difficult than the challenges faced by an ornithologistcollecting similar information on birds foraging in the woods. The collection of such micrositeinformation 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 tocommunity processes. We suggest that the potential impacts of behavior for plant communitiesand plant coexistence are similar to the roles of other functional traits (McGill et al. 2006) butare understudied. Behavioral strategies need to be more explicitly included in efforts to movefrom the individual up to the population or community (Figure 2). Through the quantification ofboth developmentally invariable pathways (those commonly associated with functional traits) andvariable pathways (those commonly associated with behavior), there is the possibility of a greaterpredictive understanding of how traits influence communities.

CONCLUSIONS

Above we show that many aspects of the soil environment act as cues, inducing alternative be-havioral types in plants. For a given cue, species vary in their behavioral type (Figure 1 andTable 2), although data are lacking for many factors that influence plant behavior. We also demon-strate that responses to multiple cues are nonadditive and are highly context dependant. Conse-quently, the resulting behaviors are not always predictable from the behavior of plants faced witheach 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-dimensionalrepresentation of multiple behaviors defines a plant’s foraging strategy. Drawing from the work ofanimal behaviorists, we suggest that behavioral syndromes and resource selection functions mayprovide descriptive models of how plants occupy the soil environment. Such models can lead toan increased understanding of possible plant responses and guide the development of predictivemodels. 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 commonlyinfluence patterns of habitat use among co-occurring individuals.

Differences in habitat use due to variation in behavioral strategies may impact species coexis-tence. We encourage the integration of foraging strategies with more traditional research on plantfunctional traits and resource strategies (Figure 2). This combined approach has the potential toenhance our understanding of both the functional ecology of plants and the processes that drivelarger-scale processes. There is substantial opportunity for future research in nearly all aspects ofplant 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 stochasticprocesses shaping root growth and development. Genetic variation in the degree ofinstability expressed suggests this stochasticity may have an adaptive role in nutrientforaging. There is a need to quantify stochasticity for more taxa and determine its rolein plant foraging. The application of the existing theory of animal movement (e.g., Levywalk; Humphries et al. 2010) to root growth may provide insights into nutrient foragingstrategies.


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2. More theoretical and empirical work is needed to understand how plants respond toneighbor 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 compari-son. Early theoretic work focused primarily on intraspecific competition among annualplants. 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 theoreticalapproaches.

3. Studies documenting behavioral responses to microbial competitors are nearly nonexis-tent. Given the ubiquitous nature of plant-microbial interactions in natural systems, thisis a substantial gap in our current understanding. A first step would be to determine whatbehavioral types exist, such as no response, avoidance, and aggregation.

4. Similar to microbial competitors, there are few studies or theories describing how rootenemies (e.g., predators and pathogens) alter plant root placement strategies. In con-trast, trade-offs between predation risk and foraging reward are among the most classicexamples of contingencies in behavioral type (Lima 1998). We suggest that roots needto be directly observed through time (e.g., minirhizotron cameras or other methods),allowing for differentiation between root removals by herbivores (root deaths) comparedwith direct behavioral adjustments of root placement (root births).

5. Mycorrhizal fungi draw significant resources from their host plants, and the host plantsdraw a variety of resources from the fungi. Viewing this relationship as reciprocal par-asitism explicitly recognizes conflicts of interest among the partners, with uncertainimplications for the consequences of differential behavioral types in the host plant.Furthermore, it is common in animal host-parasite systems for the parasite to manipu-late host behavior, typically in a way that benefits the parasite. Whether this is commonin plant-fungal symbiosis is unknown. More data are needed describing whether plantsalter behavior as a function of fungal colonization from a diversity of species. Further-more, the consequences of differential behavioral types for both the host and the fungiare completely lacking and are critical to understanding the behavioral ecology of thisinteraction.

6. Most studies of plant root foraging have focused on nutrients as a group. This likelyresembles some natural situations in which there can be high spatial covariance amongconcentrations of different resources (e.g., urine patch). However, other situations mayresult in the elevation of specific nutrients. How plants respond to specific nutrients ispoorly understood, particularly if different resources vary at different locations within asingle root system. Furthermore, despite water being critical for all plants, it has beengreatly 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 forag-ing and movement (e.g., ethograms). Plant ecologists do not and instead tend to rely onroot placement or foraging precision as representative of plant foraging behavior. Plantsneed to be screened for additional, repeatable aspects of foraging behavior. This needsto 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 todetermine how plant behavior is adjusted in the presence of multiple cues. Such dataneed to be effectively synthesized into a usable form; resource selection functions maybe a possible tool, although there exist many alternatives in animal behavior and nichemodeling. Theory in this area is also underdeveloped, and concepts such as behavioralsyndromes should be considered.

9. There are few conceptual or empirical studies that link foraging strategies to largerpatterns of communities and populations. Particularly rare are field studies, and insteadthe focus has been on greenhouse and mesocosm experiments. An initial step would be todevelop RSPFs from in situ plant communities, helping to identify consistent cues thatinfluence 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 thatmight be perceived as affecting the objectivity of this review.

ACKNOWLEDGMENTS

We thank many colleagues for extensive discussions leading to the ideas presented here, includingMark Boyce, Hans de Kroon, Liesje Mommer, Colleen St. Clair, Pete Hurd, Joel S. Brown, andCam 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 workwas funded by an NSERC Discovery Grant and Accelerator Supplement awarded to J.F.C.

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ES42-FrontMatter ARI 11 October 2011 16:5

Annual Review ofEcology, Evolution,and Systematics

Volume 42, 2011Contents

Native Pollinators in Anthropogenic HabitatsRachael Winfree, Ignasi Bartomeus, and Daniel P. Cariveau � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

Microbially Mediated Plant Functional TraitsMaren L. Friesen, Stephanie S. Porter, Scott C. Stark, Eric J. von Wettberg,

Joel L. Sachs, and Esperanza Martinez-Romero � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �23

Evolution in the Genus HomoBernard Wood and Jennifer Baker � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �47

Ehrlich and Raven Revisited: Mechanisms Underlying Codiversificationof Plants and EnemiesNiklas Janz � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �71

An Evolutionary Perspective on Self-Organized Division of Laborin Social InsectsAna Duarte, Franz J. Weissing, Ido Pen, and Laurent Keller � � � � � � � � � � � � � � � � � � � � � � � � � � � �91

Evolution of Anopheles gambiae in Relation to Humans and MalariaBradley J. White, Frank H. Collins, and Nora J. Besansky � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 111

Mechanisms of Plant Invasions of North America and European GrasslandsT.R. Seastedt and Petr Pysek � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 133

Physiological Correlates of Geographic Range in AnimalsFrancisco Bozinovic, Piero Calosi, and John I. Spicer � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 155

Ecological Lessons from Free-Air CO2 Enrichment (FACE) ExperimentsRichard J. Norby and Donald R. Zak � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 181

Biogeography of the Indo-Australian ArchipelagoDavid 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 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 205

Phylogenetic Insights on Evolutionary Novelties in Lizardsand Snakes: Sex, Birth, Bodies, Niches, and VenomJack W. Sites Jr, Tod W. Reeder, and John J. Wiens � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 227

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ES42-FrontMatter ARI 11 October 2011 16:5

The Patterns and Causes of Variation in Plant Nucleotide Substitution RatesBrandon Gaut, Liang Yang, Shohei Takuno, and Luis E. Eguiarte � � � � � � � � � � � � � � � � � � � � � 245

Long-Term Ecological Records and Their Relevance to Climate ChangePredictions for a Warmer WorldK.J. Willis and G.M. MacDonald � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 267

The Behavioral Ecology of Nutrient Foraging by PlantsJames F. Cahill Jr and Gordon G. McNickle � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 289

Climate Relicts: Past, Present, FutureArndt Hampe and Alistair S. Jump � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 313

Rapid Evolutionary Change and the Coexistence of SpeciesRichard A. Lankau � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 335

Developmental Patterns in Mesozoic Evolution of Mammal EarsZhe-Xi Luo � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 355

Integrated Land-Sea Conservation Planning: The Missing LinksJorge G. Alvarez-Romero, Robert L. Pressey, Natalie C. Ban, Ken Vance-Borland,

Chuck Willer, Carissa Joy Klein, and Steven D. Gaines � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 381

On the Use of Stable Isotopes in Trophic EcologyWilliam J. Boecklen, Christopher T. Yarnes, Bethany A. Cook, and Avis C. James � � � � 411

Phylogenetic Methods in BiogeographyFredrik Ronquist and Isabel Sanmartın � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 441

Toward an Era of Restoration in Ecology: Successes, Failures,and Opportunities AheadKatharine N. Suding � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 465

Functional Ecology of Free-Living Nitrogen Fixation:A Contemporary PerspectiveSasha C. Reed, Cory C. Cleveland, and Alan R. Townsend � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 489

Indexes

Cumulative Index of Contributing Authors, Volumes 38–42 � � � � � � � � � � � � � � � � � � � � � � � � � � � 513

Cumulative Index of Chapter Titles, Volumes 38–42 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 517

Errata

An online log of corrections to Annual Review of Ecology, Evolution, and Systematicsarticles may be found at http://ecolsys.annualreviews.org/errata.shtml

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