trade-offs between plant growth and defense against insect ... · plant physiological and molecular...

PP68CH19-Agrawal ARI 6 April 2017 9:38

Trade-Offs Between PlantGrowth and Defense AgainstInsect Herbivory: An EmergingMechanistic SynthesisTobias Zust1 and Anurag A. Agrawal21Institute of Plant Sciences, University of Bern, 3013 Bern, Switzerland;email: [email protected] of Ecology and Evolutionary Biology and Department of Entomology, CornellUniversity, Ithaca, New York 14853; email: [email protected]

Annu. Rev. Plant Biol. 2017. 68:513–34

First published online as a Review in Advance onJanuary 30, 2017

The Annual Review of Plant Biology is online atplant.annualreviews.org

https://doi.org/10.1146/annurev-arplant-042916-040856

Copyright c© 2017 by Annual Reviews.All rights reserved

Keywords

resource allocation, growth rate estimation, induced plant defense, leafeconomics spectrum, plant–insect interactions, signal transductionnetworks

Abstract

Costs of defense are central to our understanding of interactions between or-ganisms and their environment, and defensive phenotypes of plants have longbeen considered to be constrained by trade-offs that reflect the allocation oflimiting resources. Recent advances in uncovering signal transduction net-works have revealed that defense trade-offs are often the result of regulatory“decisions” by the plant, enabling it to fine-tune its phenotype in responseto diverse environmental challenges. We place these results in the contextof classic studies in ecology and evolutionary biology, and propose a unify-ing framework for growth–defense trade-offs as a means to study the plant’sallocation of limiting resources. Pervasive physiological costs constrain theupper limit to growth and defense traits, but the diversity of selective pres-sures on plants often favors negative correlations at intermediate trait levels.Despite the ubiquity of underlying costs of defense, the current challengeis using physiological and molecular approaches to predict the conditionswhere they manifest as detectable trade-offs.

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ANNUAL REVIEWS Further

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http://www.annualreviews.org/doi/full/10.1146/annurev-arplant-042916-040856


Trade-off:a fitness-imposedconstraint on twocompeting functions,resulting in patterns ofnegative associationbetween the functions

Contents

INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514THEORETICAL CONSIDERATIONS OF COSTS OF DEFENSE

AND GROWTH–DEFENSE TRADE-OFFS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515GENETIC COSTS OF DEFENSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 518COSTS OF RESISTANCE ACROSS PLANT COMMUNITIES AND THE

LEAF ECONOMICS SPECTRUM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 518INDUCED DEFENSE AND PHENOTYPIC COSTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521THE MOLECULAR SIGNAL TRANSDUCTION NETWORK THAT

REGULATES GROWTH AND DEFENSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521GROWTH RATE ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527

INTRODUCTION

For the past several decades, research on costs of plant defense has been a highly active area in plantbiology, ecology, and evolution (16, 23, 25, 49, 103, 123). The basis for this interest comes froma variety of questions asked by plant scientists, key among which are (a) why plant individuals orcommunities express intermediate levels of defense and do not evolve higher levels (78, 93), (b) whybreeding for enhanced yield can result in the unintentional loss of defenses (21), and (c) why levelsof defense typically increase after damage (induced defense) (55). The commonly invoked answerto all three questions is that defensive traits have a cost that results in trade-offs with other plantfunctions, such as growth or reproduction. In this review, we revisit the theoretical considerationsthat underlie growth–defense trade-offs and place them in the context of recent findings fromplant physiological and molecular biology to outline an updated predictive framework for thestudy of costs of defense.

Growth of vegetative tissue is a key physiological process for the majority of a plant’s lifetimeand an important component of overall plant performance (Figure 1). Plant growth rate—thegain in biomass per unit time—is a function of the conversion of primary metabolites into cellsand cellular building blocks and depends on the balance of resource sources (leaves and rootsfor carbohydrates and nitrogen, respectively) and sinks (122). Herbivory can severely impair plantgrowth and often favors better-defended individuals, yet any redirection of energy and matter fromprimary metabolism to defense should reduce growth and reproduction. Thus, understanding themechanisms that govern trade-offs between growth and defense is important, particularly giventhat rapid growth is a desirable trait in many agricultural crop species.

For costs of defense to affect evolutionary processes in natural systems, only those costs thatultimately impact plant fitness are relevant, but true plant fitness (i.e., the number of successfuloffspring in future generations) is exceedingly difficult to measure. For practical reasons, mostexperiments therefore rely on surrogate fitness measures. Measures of seed set are commonlyused (e.g., 76, 78, 94), but these capture only female fecundity and typically neglect the otherhalf of fitness (siring of seeds by pollen). Alternatively, plant growth is often used as a measureof plant performance (e.g., 6, 54, 88, 137), particularly in long-lived species. It is crucial to notethat all surrogate measures are highly context dependent, and their use may be more or lessappropriate in different experiments. Differences in growth rate can be highly relevant for successat key stages, such as the colonization of new habitat patches involving a scramble for resources.

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Fitness

Resources

Herbivory

CompetitionGrowth

Defense

Reproduction

Figure 1Conceptual diagram of plant fitness determination via the combined effects of resource allocation tocompeting plant functions (green arrows), resource-based trade-offs (gray lines), and ecological interactions(black arrow and lines). Arrows indicate positive effects; lines with bars at the ends indicate negative effects.The sizes of the green arrows represent the relative amounts of allocated resources. Although growth,defense, and reproduction should ultimately trade off, equal allocation to two of the three functions may alsoresult in positive covariance. Allocation to growth returns resources that may be reallocated to otherfunctions. A higher growth rate may increase competitive dominance and reduce the negative impacts ofcompetitors. Allocation to defense is diffuse, and individual defense traits may impose only minor drains onthe overall resource pool, but overall allocation to defense should negatively affect growth. The costs ofdefense are offset at least partly by a reduced impact of herbivory.

Opportunity costs:losses of competitivestatus early in plantontogeny as a result ofallocation to defenseinstead of growth

Metabolic costs:the total energy andresource expendituresfor the production andmaintenance of adefense trait, includingmetabolic pathwaysand building blocks

At such key stages, small differences in growth can have large impacts on the overall fate of aplant (opportunity costs) (25). For example, in cases of simultaneous germination of annual plants,growth rates measured on individual plants proved to be the best predictor of the outcome of directplant–plant competition (136) and of the eventual genotypic dominance in a selection experimentwith intense competition (33).

In this review, we specifically focus on trade-offs between investment in defense and plantgrowth (as opposed to other components of fitness) for two reasons. First, as discussed above, weassume that growth ultimately impacts fitness under many conditions and that growth represents acommon currency that can be readily measured in all plant species. Second, and more importantly,plant growth serves as an intermediate stage between resource allocation and fitness and is of keyimportance to link the molecular underpinnings and physiological basis of plant allocation totrade-offs. We highlight recent progress in the understanding of plant regulatory networks thatprovide a mechanism for plants to fine-tune their phenotype and maximize fitness under limitedresources. Finally, we discuss a set of essential methods that should be part of the toolbox ofbiologists interested in studying costs of defense.

THEORETICAL CONSIDERATIONS OF COSTS OF DEFENSEAND GROWTH–DEFENSE TRADE-OFFS

Metabolic costs involved in the expression of a defense are ubiquitous and can be considerable.Each defensive trait or compound requires not only precursor molecules from a plant’s primarymetabolism for synthesis, but also energy and precursors for the machinery involved in syn-thesis, modification, transport, and maintenance or storage (38). Gershenzon (39) estimated theexpenditures on all metabolic pathways involving terpenoid synthesis and concluded that thesecompounds are among the most energetically expensive defensive metabolites a plant may pro-duce, even though they consist largely of carbon, which is not typically a limiting resource inplants. Similarly, Bekaert et al. (14) used a mathematical metabolic model of Arabidopsis thalianato demonstrate that synthesis of the main defensive compounds of the Brassicaceae, the glucosi-nolates, increases the plant’s photosynthetic requirement by at least 15%. Nonetheless, metabolic

www.annualreviews.org • Trade-Offs Between Plant Growth and Defense 515

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Allocation costs:reductions in growthor reproduction as adirect consequence ofdiversion of limitingresources to defenseprocesses

Genetic costs:trade-offs betweengrowth and defensetraits based onunderlying(quantitative ordiscrete) geneticvariation

Ecological costs:side effects of a plant’sdefense investment onits ecologicalinteractions, such asincreased susceptibilityto specialist herbivoresor negative impacts onmutualists

costs will not always result in detectable trade-offs with growth (7, 16, 63), or indeed affect fitness.Understanding the conditions under which trade-offs manifest is thus of central importance.

A trade-off between two functions could originate from at least three, non-mutually-exclusivemechanisms: (a) Traits may be directly limited by competition for resources (allocation costs),(b) traits may be linked genetically through pleiotropy or linkage disequilibrium (genetic costs), or(c) coexpression of traits may be penalized depending on a plant’s environment (energetic limitationor ecological costs). The relative importance of the different mechanisms depends at least in parton the hierarchical level of a trait. Defense as a whole-plant phenotype (often quantified as theinverse of herbivore consumption) and growth rate are the product of many underlying traitsand functions (26). The overall defense level of a plant is a function of lower-level traits related toprimary metabolism, such as physiological leaf properties (e.g., protein and lignin content), as wellas the concentrations of defensive secondary metabolites. Similarly, growth rate is a function oftraits related to nutrient acquisition and photosynthetic function. With decreasing trait hierarchy,fewer genes are generally involved in the expression of a trait, making direct genetic costs morelikely to occur. Indeed, trade-offs with other functions involving genetic costs have been frequentlydemonstrated for low-level traits (6, 76, 101, 107, 129).

Direct competitive resource limitation of two traits (allocation costs) is perhaps the most com-monly considered causal driver of trade-offs (102), but this view may be an oversimplification ofcomplex plant allocation processes (55) (Figure 1). For example, if a plant is able to compensatefor an increase in allocation to either growth or defense by reducing allocation to a third process,no growth–defense trade-off may be observed, despite significant changes to the overall plantphenotype. Pleiotropy and linkage disequilibrium may explain how trade-offs can have a geneticbasis but provide little explanation for why they are maintained over evolutionary time. We there-fore propose a conceptual framework that emphasizes selective pressures as key elements in thecreation and maintenance of trade-offs at the physiological level.

Within the physiological limits determined by the total amount of resources available, therelative fitness achieved by a specific trait combination is primarily determined by past naturalselection and current environmental conditions (Figure 2). Very low values of either growth ordefense are commonly disfavored by natural selection, as evidenced by the competitive advantageof faster-growing individuals in colonization scenarios (33, 136) and the severe herbivore damageexperienced by plants with defenses that have been artificially abolished (58, 59).

Opposing the selection to increase trait levels are a set of mechanisms such as diminishingreturns of traits with increasing investment, as well as extrinsic selective pressures such as ecologicalcosts (102) (see sidebar titled Trait Limitation by Ecological Costs). The balance between opposingselective pressures may favor intermediate levels of both growth and defense traits (Figure 2).Indeed, stabilizing selection on intermediate defense levels because of the associated costs has beenrepeatedly demonstrated (20, 78, 91, 124), but note that strong selection may occasionally drivetrait levels to the edge of physiological limits (93, 112). High levels of coexpression for growth anddefense may thus be physiologically possible at the expense of a third function, but will likely bemaladaptive in most natural environments and result in the removal of a trait combination froma population by natural selection.

This framework highlights three key considerations: First, trade-offs may be absent in environ-ments that lack specific selection pressures (such as environments lacking competition); second,trade-offs may be undetectable if plants are examined outside of their natural environment; andthird, if plant growth and defense are the only traits of interest, high levels of both may be achievedby selective breeding. In the following sections, we provide an overview of some of the major ap-proaches that have been used to detect and quantify costs of defense and place them in the contextof our framework.

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Com

petit

ion

Herbivory Costs

Cost

s

Gro

wth

Defense

Physiological trait limit

Figure 2Hypothetical relationships among growth, defense, and plant fitness. The expression of traits is constrainedby an upper physiological limit (solid line), at which all resources are allocated exclusively to either growth ordefense. Reduced resource availability may shift this limit inward (dashed line). Within physiological limits,the fitness landscape is variable, and the shape of the fitness “ridge” (blue) is affected by natural selection(green arrows). Pervasive costs of defense in generally resource-limited environments will often favorintermediate trait levels. Natural selection will favor genotypes at peak fitness (black symbols) while penalizingmore extreme genotypes (gray symbols), resulting in negative correlations and detectable trade-offs.

TRAIT LIMITATION BY ECOLOGICAL COSTS

Ecological costs are easily overlooked in the study of plant growth and defense because they manifest themselvesonly through interactions with the biotic and abiotic environment of a plant (102). For example, resource allocationto defense can have negative impacts on other fitness-relevant functions that affect such interactions—e.g., highlevels of a defense trait may deter mutualists such as pollinators and indirectly reduce seed production (109). Buteven if herbivory is the main environmental interaction of a plant, allocation to any one defense trait may notnecessarily increase plant resistance to all herbivores, particularly in interactions involving coevolved, specializedinsects with high resistance to their host plant’s defensive arsenal (32, 100). High allocation to a defense trait inresponse to a generalist herbivore may in fact increase susceptibility to specialists (e.g., 40, 43, 100). This effect islikely exaggerated if different defensive compounds are subject to trade-offs.

As is the case for defense, growth rates are often well below the physiological maxima in many species (8, 81),and although direct costs are likely important (98), growth rates are also likely to be subject to ecological costs.Increased growth rates are generally the direct result of increased photosynthetic efficiency (quantified as the netassimilation rate), which is achieved by thicker, more chlorophyll-rich leaves (137). For any given level of defense,herbivores will thus likely prefer faster-growing, more nutritious individuals and exert negative selection on thegrowth phenotype.


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GENETIC COSTS OF DEFENSE

Because of their importance for the microevolutionary dynamics of populations, a wide range ofstudies have focused on genetic costs of specific defensive traits of interest (e.g., 6, 54, 88, 137).In these studies, traits of low-defense and high-defense plant genotypes are typically measuredin environments without herbivores, and the cost of defense is inferred as the relative growthor fitness reductions suffered by high-defense genotypes relative to low-defense genotypes. Suchgenotype comparisons allow for direct predictions of population change in response to herbivoreselection pressures. However, as discussed above, the detection of potential trade-offs using thisapproach relies to a large extent on the specific selective environment in the source population,and thus may not unequivocally demonstrate a mechanistic, causal basis of costs of defense.

Stressful environmental conditions and nutrient limitation are hypothesized to increase thelikelihood of the manifestation of trade-offs (15, 63). Decreasing levels of resources will likelyconstrain the potential trait space of the fitness landscape (Figure 2) and increase penalties forhigh trait expression levels (13). Indeed, a set of milkweed genotypes grown at two nutrient levelsexhibited a stronger negative correlation between growth rate and defense (cardiac glycosideconcentration) at low nutrient availability (137). As an alternative to using natural genotypes fromenvironments with uncertain selective pressures, a powerful method to demonstrate trade-offs isthe manipulation of selection regimes using either artificial selection (107, 108) or natural selectionby manipulating the presence of herbivores (2, 135).

Studies that manipulate selection have the advantage of starting from a known phenotypic pointof reference that serves as a control for the comparison with the altered genotypic compositionof a population. If a specific selection regime favors one trait and increases its expression, aconcordant decrease in another trait provides strong support for genetic trade-offs between thesetraits. Guiding natural selection via herbivore manipulation has the added benefit of removing theneed for the experimenter to artificially select traits of interest, because herbivore feeding and anyassociated ecological effects will favor traits with the strongest effect on herbivore performance andplant fitness. For example, selection by two specialist aphids exerted strong opposing selection ona biochemical genetic locus in A. thaliana that had not previously been associated with resistanceto aphids (135).

Among the caveats that make both natural and artificial selection approaches challenging isthe often long life span of plants. Genetic approaches have therefore emerged as an alternativemethod to manipulate trait levels and observe corresponding changes in other traits (22, 92, 106,130, 136). Genetic manipulation generally limits the differences between a manipulated genotypeand a control genotype to a single altered gene, and such approaches may more thoroughly explorethe full range of physiologically possible trait levels. Nonetheless, genetic manipulation studiesof constitutive defense have thus far employed mostly a sledgehammer approach by targetingmajor-effect loci that radically change the defensive phenotype of a plant (e.g., 59, 136), but atleast in some cases, natural selection may act on such major-effect loci (17, 31, 118). Exploringthe link between targets of genetic manipulation and naturally occurring genetic variants that aresubject to natural selection is a critical frontier to make the study of costs of defense both morerigorous and evolutionarily relevant.

COSTS OF RESISTANCE ACROSS PLANT COMMUNITIES AND THELEAF ECONOMICS SPECTRUM

In an attempt to find general patterns in biology, growth–defense trade-offs are increasinglystudied not only in individual plants or species, but also across many species of an ecosystem—for

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example, trees in a tropical rain forest (24, 37) or plants in a grassland community (71). A major axisof cross-species leaf physiology not directly related to defense has been termed the leaf economicsspectrum (96, 125). This concept describes correlations among leaf physiological traits that governresource investments by plants to determine strategies ranging from resource acquisitive (fastgrowing, short lived) to resource conservative (slow growing, long lived).

The leaf economics spectrum has had some success in predicting trade-offs between growthand defense investment (44, 82), and multiple studies using cross-species comparisons have foundevidence for such trade-offs (24, 37, 71). The selective pressures that shape a plant’s fitness land-scape may vary widely at a local scale, but at a more global scale, they may average out and reveala general disfavoring of high coexpression of defense and growth (Figure 2). However, althoughlarge-scale species comparisons can provide important support for the existence of trade-offs, thereare both conceptual and methodological pitfalls that require careful interpretation and make thisapproach prone to false conclusions from a mechanistic perspective.

The implicit assumption of comparative studies with a community focus is that growth anddefense are major axes of differentiation for all species of that community. Herbivory shouldthus act as a strong force that reduces plant performance, herbivores should respond to plantdefensive traits in similar ways, and herbivore impacts should be negatively correlated with plantdefense investment. In reality, most plant species are likely to have differentiated along manydifferent axes in response to diffuse coevolution with a whole community of herbivores and otherenvironmental variables (52, 89, 90). Thus, although trade-offs are likely drivers of growth anddefense levels among genotypes within each species, the same is not always true across species.

Because different plant species produce widely different mixtures of defensive chemicals, broadcomparative studies among distantly related species of a natural community often forgo quan-tification of such specific defenses and instead focus on traits that are easily quantifiable across awide range of species (e.g., leaf thickness or ash content) (72, 80). Such analyses can result in theerroneous conclusion that secondary metabolites are not important for plant defense (see 5). Acommon alternative approach is to focus on a high-level measure of defense, such as the inverseof plant damage by reference herbivores in a bioassay or a community of attackers in the field(57, 103). The results of biodetector studies that use generalist herbivores or total damage inthe field come with several caveats. First, the relative selective impacts of generalist herbivorescompared with specialists are unclear. Even though slugs and browsing mammals are prevalentand can be devastating when they feed on a plant, they may be too undiscriminating to exertconsistent selective pressures on individual plant species. Because specialists and generalists mayrespond divergently to the same plant secondary metabolites, measures of total damage in thefield are typically difficult to interpret (5). Second, few insect herbivores are true generalists, andlaboratory favorites such as Spodoptera caterpillars are not significant herbivores in most naturalplant communities. Finally, simple measures of total herbivore damage can often be driven byvariation in plant nutritional quality, which may not be directly related to other measures of de-fense investment. All three points highlight that although generalist herbivory can be a usefulmeasure of defense, it may often be unrelated to the costs and benefits of the defensive traits thatare most prominently under natural selection in the field.

The implementation of the leaf economics spectrum for the study of costs of defense is perhapsmost promising in a phylogenetically controlled set of related species. For example, Mason et al.(77) demonstrated that within a large group of wild sunflower species native to North America,higher levels of leaf toughness and tannins were associated with a more resource-conservative,slower-growing leaf economics strategy. Species in a monophyletic group originated from thesame ancestral state, and thus, as long as environmental variation is accounted for, trade-offspresent among these species likely reflect underlying costs of defense. Even so, results from such


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studies must be interpreted with care. Leaf physiological traits may well show different resultsat different scales. High leaf density, as expressed by leaf mass per area, is associated with slowgrowth of species along the leaf economics spectrum, and the same association can be found forsingle species grown in different environments (3, 137). For example, milkweed plants under nu-trient limitation grew more slowly and produced smaller, thicker leaves than those with the samegenotypes and high nutrient availability (137) (Figure 3a). However, variation in functional traitslikely has different causes between and within environments. Within an environment, photosyn-thetic efficiency [net assimilation rate (NAR)] may be fixed, but because of the functional andmathematical relationships between these traits, genotypes with low NAR could hypotheticallyincrease their growth by enlarging leaf area—for example, via cell extension (Figure 3b). Suchplasticity may reverse predicted correlations of the leaf economics spectrum within single-species,single-environment comparisons (137) (Figure 3a,c).

RGR (mg mg–1 day–1)

a

2.0 2.5 3.0 3.5 4.0 4.5 5.00.0

0.1

0.2

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RGR

0.10

0.15

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RGR

b c

Nutrient availability

3.0 3.5 4.0 4.5 5.0 5.5

LMA (mg cm–2) 3.1 4.14.4 5.1

0.20 0.23

High High

NAR (mg cm–2 day–1) 0.8 1.80.7 1.8

0.12 0.19

Low Low

Figure 3(a) Effects of the environment on leaf traits. Plants growing at reduced nutrient availability (left) produce smaller, thicker leaves andachieve lower growth than plants with high nutrient availability (right). Photosynthetic conversion efficiency [quantified here as netassimilation rate (NAR)] is a genotype-specific trait, but lower efficiency may be compensated for in part by water-driven cellelongation [decreasing the leaf mass per area (LMA)]. Values are extreme genotype means selected from a set of milkweed genotypesgrown at two fertilization levels (137). (b) Visualization of hypothetical compensation for reduced photosynthetic efficiency. At acommon leaf density, reduction of photosynthetic efficiency by 50% (gray circles) results in a 50% decrease in growth rate relative to acontrol state (blue circle and dashed line). Conversely, increasing leaf area via a reduction in LMA by cell elongation may increase growthrate. (c) Because of this plasticity, comparisons between species or environments may predict a negative relationship between LMA andgrowth (solid line), whereas within-species comparisons are more likely to uncover positive relationships (blue circles and dashed lines).Additional abbreviation: RGR, relative growth rate.

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Phenotypic costs:negative correlationsbetween measures ofgrowth and defensewith unknown or nogenetic contributionsto trait variation

INDUCED DEFENSE AND PHENOTYPIC COSTS

The third common approach to studying growth–defense trade-offs takes advantage of inducedresponses to herbivory, or the increased production of plant secondary metabolites followingattack by herbivores. Defense induction itself is viewed as a cost-saving strategy in the face ofunpredictable and variable herbivore pressures, where high costs may limit constitutive expressionof defense (45). Defense pathways are highly conserved across many plant species, and althoughspecies differ in their defensive traits, damage caused by chewing herbivores generally triggersa spike in the production of jasmonic acid ( JA), which induces a signaling cascade to increaseplant defense levels (50). JA induces a remarkably diverse set of defensive traits in different plantspecies, including alkaloids, oxidative enzymes, glucosinolates, cardenolides, trichomes, volatileorganic compounds, and extrafloral nectar (1). Exogenous application of JA to a plant can thereforestimulate defense induction without damaging plants or reducing their photosynthetic leaf area.A large number of studies using a wide range of plant species have demonstrated inhibitory effectsof JA and defense induction on growth (10, 27, 29, 46, 48, 51, 94, 116, 133).

Activation of the JA signaling pathway results in complex changes in a plant’s metabolism inaddition to inducing defenses. For example, Halitschke et al. (42) demonstrated that elicitationof the JA pathway in Nicotiana attenuata directly resulted in a downregulation of photosynthesis,whereas other studies showed little change in photosynthetic rate following JA elicitation in A.thaliana (10, 19). JA-mediated defense induction can also affect carbon fluxes between shoots androots (11, 36, 41, 79, 97, 99). For example, induction of N. attenuata by JA increased defensivecompounds in leaves but decreased sugar and starch reserves in roots, impairing subsequent re-growth potential from roots (74). Similarly, treatment of A. thaliana leaves with JA resulted ina temporary increase in the allocation of new photoassimilates to roots and a more long-termallocation to treated leaves, resulting in increased accumulation of defensive compounds (36).The changes in allocation and decrease in growth following induction are generally interpretedas a cost of increasing the levels of a defense, although the comparison of plant growth betweeninduced and uninduced plants may include other strategic allocation changes in the plant (4, 13,47, 84). The altered carbon allocation and storage may thus be driven partly by costs of defenseand partly by the plant’s anticipation of future herbivory and regrowth.

Studies of induced defense generally focus on single genotypes (or uncontrolled mixtures ofgenotypes), and costs detected in this approach are phenotypic. Estimates of phenotypic costs willoften be different from genetic costs, even when considering the same defensive traits. Nonetheless,the major advantage of such comparisons is the potential for a more mechanistic understandingof the underlying metabolic costs, particularly if the study uses targeted approaches, such as thequantification of 15N allocation to nicotine in tobacco following induction (12). A combinationof approaches and quantification of induction-mediated trade-offs across natural genotypes of aspecies promises to be a powerful tool to elucidate the full fitness landscape that drives plantdefensive phenotypes.

THE MOLECULAR SIGNAL TRANSDUCTION NETWORK THATREGULATES GROWTH AND DEFENSE

Recent advances in the investigation of regulatory pathways that mediate growth–defense trade-offs have renewed interest in the study of costs of defense (46, 51). Different phytohormones havelong been known to affect each other through networks of stimulating and inhibiting interactions(65) (Figure 4), including the prominent example of antagonism (or crosstalk) between the defense


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phyB

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DELLA

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COI1JAZ

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Figure 4Working model of the plant defense signaling network for regulation of growth–defense trade-offs.Herbivory-associated cues, such as wounding [damage-associated molecular patterns (DAMPs)] and oralsecretion [herbivory-associated molecular patterns (HAMPs) and fatty acid conjugates (FACs)], activatemitogen-activated protein kinases (MAPKs). Wound-induced protein kinases (WIPKs) primarily stimulatejasmonic acid ( JA), and salicylic-acid-induced protein kinases (SIPKs) additionally stimulate ethylene (ET)and suppress salicylic acid (SA) synthesis. In rice (light yellow highlight), an early trade-off is regulated by thetranscription factor WRKY70, which activates JA and ET synthesis genes but suppresses gibberellin (GA)and SA synthesis genes. Note that the function of WRKY70 appears to be highly species specific. The GApathway is partially under the control of external stimuli (e.g., shading) and results in the activation ofgrowth-promoting transcription factors, such as phytochrome-interacting factor (PIF), whereas the JApathway activates the defense-promoting transcription factor MYC. Phytohormones further interact vialargely unknown mechanisms (dashed lines). In particular, the GA and JA pathways act as reciprocalsuppressors via an interaction of their respective transcription factors and suppressor proteinsJASMONATE ZIM DOMAIN ( JAZ) and DELLA. Arrows indicate positive effects; lines with bars at theends indicate negative effects. Additional abbreviations: ACS2, 1-AMINO-CYCLOPROPANE-1-CARBOXYLATE SYNTHASE 2; COI1, CORONATINE INSENSITIVE; GA20ox7, gibberellin 20–oxidase 7;GID1, GIBBERELLIN-INSENSITIVE DWARF 1; ICS, isochorismate synthase; JA-Ile, JA-isoleucineconjugate; LOX, lipoxygenase; phyB, phytochrome B.

hormone JA and salicylic acid (SA) (111). Additional components of the plant signaling networkcontinue to be identified and understood, including feedback loops of defensive metabolites onphytohormones (e.g., 18, 56). An emerging consensus highlights the close regulatory control ofgrowth and defense by the plant and identifies negative associations between growth and defensenot as the direct result of allocation costs, but rather as prioritization of one process over another(19, 51, 60, 73, 99, 104, 121, 128). Trade-offs between growth and defense thus may more oftenreflect the range of trait combinations that achieve optimal fitness, rather than representing hardphysiological limits.

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Levels of defense are regulated by central nodes in the plant signaling network, and manip-ulation of these nodes can have significant consequences for the defensive phenotype of a plant.For example, induction of defensive nicotine in N. attenuata is mediated by JA and involves con-jugation of JA with isoleucine (Ile) to create JA-Ile and the binding of JA-Ile to the receptorCORONATINE INSENSITIVE 1 (COI1). Mutants deficient in JA biosynthesis show an 80%reduction in induced nicotine levels (74, 75, 120). Plants impaired in the conjugation of JA to JA-Ile exhibited only a 30% reduction of induced nicotine (120), whereas silencing of COI1 resultedin a 50% reduction of induced nicotine (86). The variable phenotypic effects of the various nodeshighlight their potential usefulness for the manipulation of growth–defense phenotypes, but alsothe need for a better understanding of unintended side effects of such manipulations under naturalconditions.

The study of natural variation in growth–defense prioritization among genotypes promises toprovide microevolutionary insights into the fate of different defense allocation strategies, yet toour knowledge, few studies have attempted this. As an example, Todesco et al. (113) demonstratedhow variation at a single genetic locus associated with the JA signaling pathway accounted for aconsiderable fraction of the variation in the growth–defense phenotypes of a large set of naturalgenotypes of A. thaliana. A comprehensive approach to studying the growth–defense signal trans-duction network should include both among-genotype and across-species comparisons and shouldtherefore allow the combination of the currently separate fields of inducible defenses, genetic costs,and community-wide plant defensive strategies. Below, we discuss the current knowledge of keynodes in the plant signal transduction network that most likely determine a plant’s allocationdecisions and represent the targets for such comparative studies.

Herbivore recognition and defense responses in plants involve complex regulatory and phy-tohormonal pathways (69, 79, 128, 131). Herbivore damage releases general damage-associatedmolecular patterns (DAMPs) and often involves specific herbivore-associated molecular patterns(HAMPs) such as salivary proteins [e.g., fatty acid conjugates (FACs)] (127). DAMPs, HAMPs, andFACs activate mitogen-activated protein kinases (MAPKs) (127, 132), which initiate signal trans-duction pathways that result in both increased defense and a slowed rate of growth (79). MAPKsare therefore important switches that can be disabled or overexpressed to manipulate inducedresponses or constitutive levels of defense to elucidate the phenotypic costs of defense induction.

MAPKs differ in their specificity and connectivity with the signal transduction network andthus offer the potential for sophisticated manipulation of plant defense strategies. For example,wound-induced protein kinase (WIPK) is a direct positive regulator of JA and downstream defenseresponses, whereas salicylic-acid-induced protein kinase (SIPK) acts as both a positive regulatorof JA and ethylene and an inhibitor of SA (79). In a defense-elicitation experiment using oralsecretions of caterpillars, WIPK-silenced N. attenuata plants accumulated less JA and benefitedfrom higher growth and seed production relative to control plants. By contrast, SIPK-silencedplants accumulated high levels of SA and did not benefit from reduced JA sensitivity, presumablybecause of the higher costs associated with SA-mediated defense (79). It is unclear to what extentgenetic variation in MAPKs is linked to genotypic variation of natural plants in constitutive defenselevels and their inducibility.

The WRKY transcription factors represent a second important class of key regulators of de-fense (69, 131). A comprehensive study of signaling in rice demonstrated that the MAPK-activatedtranscription factor WRKY70 is an early positive regulator of JA signaling following herbivory byleaf chewers but is also a suppressor of growth (69). WRKY70 acts to upregulate the lipoxygenase(LOX) gene involved in JA synthesis (134) (Figure 4) and concurrently inhibits the plantgrowth hormone gibberellin (GA) by suppressing the GA synthesis gene gibberellin 20–oxidase 7(GA20ox7), resulting in a stunted phenotype (69). Supplementing exogenous GA restores a normal


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growth phenotype, suggesting the causal role of GA suppression by WRKY70 (69). Overexpressionof WRKY70 results in high levels of JA and resistance to chewing herbivores but simultaneouslyincreases susceptibility to sucking herbivores of rice plants. WRKY70 also acts as an inhibitorof isochorismate synthase (ICS) (Figure 4), a key enzyme of SA synthesis (126) that is generallyrequired for resistance to sucking herbivores (119). WRKY70 could thus be partly responsible forSA–JA crosstalk (e.g., 62), although the early positioning of WRKY70 in the rice signaling cascadesuggests additional antagonism between the two hormones further downstream (Figure 4).

Current work suggests that WRKY transcription factors have roles in defense signaling indiverse plant species, and WRKY70 is generally associated with SA–JA crosstalk (67, 68). However,the signal connectivity of WRKY70 and its relative position within the regulatory network appearto be highly species specific. Overexpression of WRKY70 in A. thaliana has the opposite effect as itdoes in rice, suppressing JA synthesis and upregulating an SA response (67, 68). Similarly, althoughLi et al. (69) demonstrated that WRKY70 in rice is unaffected by exogenous JA or SA treatmentand thus concluded it to be an early regulator of plant defense signaling (Figure 4), WRKY70in A. thaliana is upregulated by SA and downregulated by JA (68). Given the evidently importantrole of WRKY70 in growth–defense trade-offs, we stress the need for cross-species comparativestudies. More generally, WRKY transcription factors appear to be key decision nodes determiningplant defensive strategies, and manipulation of these nodes is increasingly used to study the roleof different growth and defense pathways in resistance to herbivores (61, 64). However, effectiveuse of this approach will require a more comprehensive understanding of the action of differentWRKYs within one species as well as of the same WRKYs in different species.

Downstream of MAPK and WRKY in the signal transduction network, additional nodes act tofurther enforce the regulatory crosstalk between growth and defense (Figure 4). Upon activationof JA synthesis, some JA is enzymatically modified to JA-Ile (105), which indirectly activates tran-scription factors (such as MYCs) that initiate the plant’s defense responses. In uninduced plants,MYC transcription factors are suppressed by JASMONATE ZIM DOMAIN ( JAZ) proteins (34).JA-Ile-activated COI1 binds to JAZ, after which the protein complex is degraded and expressionof MYC commences (Figure 4). In an equivalent cycle, the growth-promoting phytochrome-interacting factor (PIF) is inactivated by the DELLA repressor protein (35). In full sunlight, theshade (red:far red ratio) receptor phytochrome B (phyB) suppresses both PIF and GA (19), butupon shading, phyB suppression is released and GA synthesis is stimulated. Activation of the GApathway initiates the formation of complexes between free GA and the proteins GIBBERELLIN-INSENSITIVE DWARF 1 (GID1) and DELLA. This releases PIFs from DELLA suppressionand allows them to bind to their target gene promoters and activate a growth response (35)(Figure 4). Reduced phyB activity also causes degradation DELLAs to further stimulate PIFs(66). The GA-GID1-DELLA complex interacts with SA in A. thaliana (83), and in fact shading ofplants can suppress both SA and JA responses and increase susceptibility to natural enemies (30).Interestingly, a mutually negative interaction takes place between JAZ and DELLA (110, 128);therefore, because the activation of the JA pathway results in the COI1-mediated degradation ofJAZ, JAZ no longer suppresses DELLA, which in turn strengthens the suppression of PIFs andplant growth (Figure 4).

Campos et al. (19) recently demonstrated that a dual inactivation of JAZ and phyB in A.thaliana results in a phenotype that is both well defended and fast growing. Similarly, GA com-plementation of JA-induced N. attenuata plants is sufficient to restore a normal growth phenotypewhile maintaining higher defense levels (R.A.R. Machado, I.T. Baldwin & M. Erb, manuscriptin preparation). These examples demonstrate that, at least under optimal laboratory conditions,negative trait associations are not allocation-based trade-offs, and plants can be forced to growand defend at the same time. In an important next step, such genotypes with high coexpression

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of both traits should be taken to the field (or other stressful conditions) to test whether theysuffer fitness consequences. The power to directly manipulate aspects of primary and secondarymetabolism highlights the need for increased understanding of the plant’s regulatory networkas well as the typical challenges that plants face in field environments. Nonetheless, there maybe tremendous potential for targeted manipulation of plant defense strategies and growth thatextends beyond the typical fitness-imposed boundaries (Figure 2).

GROWTH RATE ESTIMATION

Growth rates are often the most convenient measures of plant performance, particularly for peren-nial plants. Growth rates that are measured over a relatively short period compared with a plant’sfull lifetime will vary in their predictive power for fitness, just as allocation decisions of a plantwill have different consequences at different life stages. Perhaps the strongest link between growthrate and fitness occurs at an early stage in a plant’s life, when clumps of seeds often fall into smallpatches of habitat, resulting in potentially strong competition. Differences in germination timeand growth rate at this stage can therefore have significant effects on lifetime success (e.g., 117).However, growth rate is a relevant part of plant performance at any point in time, as it representsa measure of resource allocation in real time.

As our understanding of the molecular mechanisms that underlie growth and defense increases,we must take care that the methods used to measure and quantify these phenotypes remain ap-propriate. Growth rate estimation has undergone a paradigm shift from being viewed not as afixed genetic trait but as a plastic response of the plant to its environment, at least in part becauseof new technology that has allowed researchers to capture growth responses nondestructively.Historically, growth has often been approximated as the total biomass gained after an arbitraryamount of time selected for the duration of the experiment. However, single biomass measuresare inappropriate measures of growth rate because differences in starting size are not accountedfor and the “final” size may be related more to the soil volume available (e.g., pot size) than to thegrowth rate per se (87, 88).

As an improvement over single biomass measures, the growth rate of a plant has often beenquantified as the relative growth rate. In its classic form, this rate is the difference between twolog-transformed plant size measurements from different time points of plant development dividedby the amount of time between the two time points. Because this method requires only two sizemeasurements, its simplicity and ease of use have played a major part in its persistence. Unfortu-nately, this method is based on the flawed assumption that plant growth follows an exponentialfunction. For exponential growth (linear on the log scale), the rate of growth is constant, andregardless of which time points are chosen for the size measurements, relative growth rate willalways reflect this true rate of growth. However, plants are virtually always limited by resourceavailability and size-dependent slowing of growth resulting from self-shading or increased invest-ment in structural tissues, and they normally grow at a less-than-exponential rate (98, 115). As aconsequence, the traditional measure of relative growth rate has repeatedly been demonstrated toprovide inaccurate or even false estimates of the true growth rate of plants (88, 114), which mayhave contributed to the debate about growth–defense trade-offs.

A more appropriate method to quantify plant growth rate is to fit biologically relevant growthfunctions (such as power-law or logistic functions) to plant size data and then extract the growthrate from the model parameters (85). Importantly, because these functions are more realistic, theyrequire more model parameters, which increases the necessary size measurements that need to berecorded over the period of interest during plant development. Because the primary growth rate ofa plant describes the gain of biomass per unit of existing biomass and time, the most direct methods


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to quantify growth rates are destructive sequential harvest experiments, in which subsets of plantsare harvested at regular intervals (88, 136). The main disadvantages of destructive sequentialharvests are (a) that high temporal resolution in the sampling quickly results in prohibitively largeexperiments and (b) that growth rates can be estimated only at the genotype or species level becauseeach plant individual harvested contributes a single temporal data point.

Nondestructive methods can also be used to generate repeated size data, ranging from manualmeasurement of leaf size (137) or quantification of rosette (leaf ) area from photographs (53) to

a b

c d

Figure 5Example of low-tech leaf area quantification using standard and infrared photography of a small cruciferspecies. (a) A standard photograph of a potted plant, illustrating potential issues of a complex soil matrix anduneven lighting. (b) The leaf area (black) after supervised leaf classification in the image analysis softwareImageJ. A technician chooses color thresholds to maximize correct classification of pixels. Overlaps in colorranges commonly lead to false classification of the background matrix as leaf area, requiring further manualprocessing to correct. (c) An infrared photograph of the same plant that highlights only photosyntheticallyactive tissue. The plant is illuminated with a red monochromatic light source and photographed with adigital camera modified for infrared light capture. (d) Automated leaf area classification of the infraredphotograph based on the whiteness of pixels, which requires virtually no postprocessing.

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fully automated plant phenotyping systems (9, 10, 28). As part of the introduction for their ownphenotyping system, Cruz et al. (28) provided a useful overview of the various advanced spectro-scopic methods used for plant phenotyping and highlighted their advantages and shortcomings.However, although continuous, high-accuracy monitoring of plant growth can provide valuableinsights into the dynamics of plant growth (28), such automated systems are often expensive, whichhas limited their adoption by the plant biology community.

We believe that there is a middle ground between labor-intensive manual methods and expen-sive automated systems. Leaf area quantification based on photographs is already commonly used(e.g., 53), but image processing still requires large amounts of manual input (Figure 5a,b). By con-trast, infrared imaging selectively captures photosynthetically active leaf tissue (28) (Figure 5c),which removes the need for labor-intensive image processing (Figure 5d ). Because standarddigital cameras can easily be converted to capture infrared spectra, this method is simple to useand readily available. Two-dimensional photography is particularly powerful for quantifying theearly leaf growth of rosette-forming plants; although plants naturally minimize leaf overlap toprevent self-shading, this overlap does become more significant as they grow, which consequentlyreduces the accuracy of leaf area estimates. Nonetheless, two-dimensional infrared photographypromises to be useful even for larger plants and more complex growth forms as long as standardizedphotographs are validated with plant biomass measures.

As growth rate estimation has become more sophisticated and closer to biological reality,it has become clear that growth rate is not a fixed trait in plants. As discussed above, for allgrowth functions except exponential growth, a plant’s growth rate intrinsically decreases as its sizeincreases. Furthermore, growth rates likely respond directly to the plant’s environment. The vastmajority of studies of growth–defense trade-offs measure growth rates under standard conditionsin the absence of herbivores and correlate them with the levels of defense (e.g., 88, 137). Estimatingmore sophisticated growth rates comes with the added benefit of enabling their partitioning intophysiological components relevant to the leaf economics spectrum, such as net assimilation rate,specific leaf area, and leaf/mass ratio (70, 95, 137). Thus, whereas relative growth rate capturesgrowth at the whole-plant scale, net assimilation rate measures the efficiency of the photosyntheticleaf area and as such may be much more directly related to plant allocation.

CONCLUSION

Growth–defense trade-offs are prevalent across biological systems, and their study is gaining re-newed attention through the discovery of the underlying regulatory mechanisms that governthese trade-offs. However, it is becoming apparent that detectable negative associations may onlyoccasionally arise from direct limitation of resource allocation or pleiotropy. More commonly,growth–defense trade-offs appear to result from plant allocation decisions that are intendedto maintain optimal fitness while responding to a variable environment. Hardwired phytohor-monal and regulatory signaling cascades have evolved over evolutionary time to manage resourceallocation among all plant functions that ultimately determine fitness. Although physiologicallimitations in the natural environment of a species are the ultimate drivers of growth–defensetrade-offs, these trade-offs are often attenuated under favorable conditions. A comprehensive un-derstanding of the growth–defense signal transduction network across natural genotypes and plantspecies, supported by available technology to accurately quantify plant growth, promises to resolvethe long-standing debate about the relevance of these trade-offs and to provide valuable insightsfor crop breeding.


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SUMMARY POINTS

1. In the quest to understand costs of defense with relevance to a plant’s evolutionarybiology, plant growth rate is a critically important measure to capture real-time resourceallocation decisions.

2. Meaningful measures of plant growth are crucial for the study of trade-offs, and re-quire frequent (nondestructive) size measurements and biologically appropriate statisticalmodels.

3. Although plant growth and defense can be inevitably (negatively) linked by pleiotropy,the more typical scenario may be that plant genotypes maximize allocation to manyfunctions, and that in nature, natural selection has favored genotypes that typically fallalong the growth–defense trade-off continuum with intermediate investment in bothfunctions.

4. Plants employ a series of regulatory switches to prevent costly coexpression of high levelsof growth and defense, but targeted selection or manipulation may disable these switchesand result in high levels of both traits, at least in conditions of high resource availabilityand in the absence of other stressors.

5. The leaf economics spectrum provides a valuable physiological framework to partitionaspects of plant growth into components, some of which are highly relevant to under-standing trade-offs with investment in plant defense. Species with different leaf economicsstrategies typically have distinct defense strategies.

6. Induced plant defense against herbivory, typically under the control of jasmonic acid, isa powerful tool to study resource allocation and phenotypic costs of defense, but cautionis needed in interpreting such physiological approaches in an evolutionary context.

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

During the writing of this review, we were supported by the Swiss National Science Foundation(grant PZ00P3-161472 to T.Z.), the National Science Foundation (grant NSF-DEB-1513839to A.A.A.), and the Templeton Foundation (grant to A.A.A.). We thank Mark Rausher, LindsayTurnbull, and Matthias Erb for helpful discussion and comments on the manuscript.

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Annual Review ofPlant Biology

Volume 68, 2017 ContentsFirmly Planted, Always Moving

Natasha V. Raikhel � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

Biogenesis and Metabolic Maintenance of RubiscoAndreas Bracher, Spencer M. Whitney, F. Ulrich Hartl, and Manajit Hayer-Hartl � � � �29

The Epigenome and Transcriptional Dynamics of Fruit RipeningJames Giovannoni, Cuong Nguyen, Betsy Ampofo, Silin Zhong, and Zhangjun Fei � � � � � �61

Retrograde Signals: Integrators of Interorganellar Communication andOrchestrators of Plant DevelopmentAmancio de Souza, Jin-Zheng Wang, and Katayoon Dehesh � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �85

The Structural Basis of Ligand Perception and Signal Activation byReceptor KinasesUlrich Hohmann, Kelvin Lau, and Michael Hothorn � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 109

Cell Biology of the Plant NucleusIris Meier, Eric J. Richards, and David E. Evans � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 139

Phloem-Mobile RNAs as Systemic Signaling AgentsByung-Kook Ham and William J. Lucas � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 173

Chemical Genetic Dissection of Membrane TraffickingLorena Norambuena and Ricardo Tejos � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 197

Plant Mitochondrial Genomes: Dynamics and Mechanisms ofMutationJose M. Gualberto and Kathleen J. Newton � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 225

Plastoglobuli: Plastid Microcompartments with Integrated Functionsin Metabolism, Plastid Developmental Transitions, andEnvironmental AdaptationKlaas J. van Wijk and Felix Kessler � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 253

Strigolactone Signaling and EvolutionMark T. Waters, Caroline Gutjahr, Tom Bennett, and David C. Nelson � � � � � � � � � � � � � � � 291

Zooming In on Plant Hormone Analysis: Tissue- and Cell-SpecificApproachesOndrej Novak, Richard Napier, and Karin Ljung � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 323

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Guilt by Association: A Phenotype-Based View of the PlantPhosphoinositide NetworkKatharina Gerth, Feng Lin, Wilhelm Menzel, Praveen Krishnamoorthy,

Irene Stenzel, Mareike Heilmann, and Ingo Heilmann � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 349

The Life and Death of a Plant CellMehdi Kabbage, Ryan Kessens, Lyric C. Bartholomay, and Brett Williams � � � � � � � � � � � � � 375

Genomics, Physiology, and Molecular Breeding Approaches forImproving Salt ToleranceAbdelbagi M. Ismail and Tomoaki Horie � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 405

New Strategies and Tools in Quantitative Genetics: How to Go fromthe Phenotype to the GenotypeChristos Bazakos, Mathieu Hanemian, Charlotte Trontin,

Jose M. Jimenez-Gomez, and Olivier Loudet � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 435

Novel Insights into Tree Biology and Genome Evolution as RevealedThrough GenomicsDavid B. Neale, Pedro J. Martınez-Garcıa, Amanda R. De La Torre,

Sara Montanari, and Xiao-Xin Wei � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 457

Defense Priming: An Adaptive Part of Induced ResistanceBrigitte Mauch-Mani, Ivan Baccelli, Estrella Luna, and Victor Flors � � � � � � � � � � � � � � � � � � � 485

Trade-Offs Between Plant Growth and Defense Against InsectHerbivory: An Emerging Mechanistic SynthesisTobias Zust and Anurag A. Agrawal � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 513

The Role of Plant Innate Immunity in the Legume-RhizobiumSymbiosisYangrong Cao, Morgan K. Halane, Walter Gassmann, and Gary Stacey � � � � � � � � � � � � � � 535

Plant Biodiversity Change Across Scales During the AnthropoceneMark Vellend, Lander Baeten, Antoine Becker-Scarpitta, Veronique Boucher-Lalonde,

Jenny L. McCune, Julie Messier, Isla H. Myers-Smith, and Dov F. Sax � � � � � � � � � � � � 563

Errata

An online log of corrections to Annual Review of Plant Biology articles may be found athttp://www.annualreviews.org/errata/arplant

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trade-offs between plant growth and defense against insect ... · plant physiological and molecular...

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