# Suppression of Plant Resistance May Be a Common Trait Among Adapted Herbivores

Jéssica Teodoro-paulo, Carlos Fernandes, Lin Dong, Sara Magalhães, Alison B Duncan, Juan M Alba, Merijn R Kant

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Jéssica Teodoro-paulo, Carlos Fernandes, Lin Dong, Sara Magalhães, Alison B Duncan, et al. Suppression of Plant Resistance May Be a Common Trait Among Adapted Herbivores. Ecology and Evolution, 2025, 15 (7), 10.1002/ece3.71706. hal-05389221

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RESEARCH ARTICLE

OPEN ACCESS

# Suppression of Plant Resistance May Be a Common Trait Among Adapted Herbivores

Jéssica Teodoro-Paulo<fnref n="1" />,²,³ ID | Carlos Fernandes<fnref n="1" /> | Lin Dong² | Sara Magalhães<fnref n="1" /> | Alison B. Duncan³ | Juan M. Alba²,⁴ ID |
Merijn R. Kant²

**Correspondence:** Jéssica Teodoro-Paulo (jessicapaulo94@gmail.com)

**Received:** 31 August 2023 | **Revised:** 17 June 2025 | **Accepted:** 21 June 2025

**Funding:** Funds were provided by an ERC consolidator grant (COMPCON, GA 725419) to SM; the Dutch Research Council (ALWOP.283; NWO-VICI 19391; EU Horizon 2020 research and innovation programme [Grant 773902—SuperPests]) to MK and FCT Doctoral grant (SFRH/BD/136416/2018) to JTP.

**Keywords:** effector 84 | effector diversity | immune suppression | intraspecific variation | salivary proteins | *Tetranychus urticae*

## ABSTRACT

Herbivores have evolved distinct strategies to cope with plant defenses prior to becoming a pest. Some evolved resistance to toxic compounds; others suppress host toxin production. These traits seem to co-occur within herbivore species, particularly among spider mites, which are major pests in many crops. The intraspecific variation within the spider mites is a model for adaptive pest evolution on crops such as tomato. Empirical data collected from nonsolanaceous wild host plants suggest that natural populations predominantly comprise individuals capable of inducing tomato defenses, while a small proportion suppress these defenses. Additionally, resistant individuals are observed only rarely within these populations. This study aimed to investigate the presence of these traits within populations adapted to tomato plants. Here, *Tetranychus urticae* populations sampled from tomato at three field sites were compared. To discriminate between mites that induce/suppress defenses and to assess their degree of resistance, the magnitude of induced defenses was measured and aligned with oviposition data. The expression of effector 84 was also assessed to determine whether its magnitude of expression is a good predictor of suppression with the magnitude of suppression. Surprisingly, we observed that suppression is the dominant phenotype in mite populations collected from field-grown tomatoes. Our results suggest that suppression may be rare only at the beginning of an herbivore's adaptive trajectory after colonization of a novel host but may rapidly become common due to natural selection. This suggests the possibility that suppression potentially represents a prevalent phenotype among host-adapted herbivores and, consequently, among pests.

## 1 | Introduction

Plants are equipped with constitutive and inducible defenses to deter herbivores (Agrawal and Karban 1999). While constitutive defenses traits are premade, like physical barriers or toxins, inducible defenses are established after herbivore attack (reviewed in Vega-Muñoz et al. 2020) and can include reinforcement of constitutive defenses. This inducible response consists of a

collection of defense programs, regulated primarily by the plant hormones jasmonic acid (JA) (Wasternack and Hause 2013) and salicylic acid (SA) (Vlot et al. 2009), and aims at reducing the quality of the plant as food. The JA pathway is mostly responsible for defense responses against necrotrophic pathogens and chewing or cell content-feeding herbivores (Walling 2000), while the SA pathway is mostly induced by biotrophic pathogens (Glazebrook 2005) and phloem-feeding insects (Kaloshian and

<footnote n="1">¹cE3c—Centre for Ecology Evolution and Environmental Changes & CHANGE—Global Change and Sustainability Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal | ²Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, the Netherlands | ³Institut Des Sciences de l'Évolution, University of Montpellier, Montpellier, France | ⁴Institute for Mediterranean and Subtropical Horticulture La Mayora (IHSM), CSIC-UMA, Málaga, Spain</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2025 The Author(s). *Ecology and Evolution* published by British Ecological Society and John Wiley & Sons Ltd.</footnote>
Walling 2005). However, the SA pathway can also be triggered
by herbivory, leading to the induction of pathogen resistance
(PR) genes (e.g., in aphids, Moran and Thompson 2001; spider
mites, Alba et al. 2015). This can include the synthesis of detri-
mental proteins, for example, by the induction of proteinase in-
hibitors that reduce herbivore protein digestion (Walling 2000),
which thereby decrease herbivore development (Karban and
Myers 1989), and population growth, promoting plant fitness
(Zavala and Baldwin 2004; Turrà et al. 2020). In response to se-
lection pressure by these defensive mechanisms, some herbivore
species (e.g., arthropods and nematodes) have evolved counter
adaptations that either enable them to neutralize (detoxify) de-
fensive metabolites (Després et al. 2007; Heckel 2014; reviewed
in Groen and Whiteman 2022) or suppress the plant's defense
metabolism (e.g., reviewed in Kant et al. 2015).

Metabolic resistance can be established through detoxification (i.e., metabolic modification, Cianfrogna et al. 2002; degradation, Heckel 2014 and secretion), sequestration (e.g., Bowers 2022), or the evolution of target-site insensitivity (Berenbaum 1986). Metabolic resistance can be efficient for several herbivore species living on a range of hosts with similar secondary chemistry. However, for generalists, stacking different modes of metabolic resistance to cope with a wider variety of defensive substances may be costly and thus readily erode under natural selection (Ali and Agrawal 2012; Blaazer et al. 2018). However, it was also shown that some generalists cope with their variable hosts by interfering with the defense pathways of different plant species (Kant et al. 2015). This interference often comes down to suppression of defenses (reviewed in Blaazer et al. 2018) and is characterized by a reduction in induced toxin accumulation (e.g., reduced nicotine accumulation by feeding caterpillars in the leaves of tobacco, Musser et al. 2002) and induced defense gene expression (Kant et al. 2004) either to intermediate levels (Alba et al. 2015) or even to levels below those of unattacked control plants (Sarmento, Lemos, Bleeker, et al. 2011; Sarmento, Lemos, Dias, et al. 2011; Schimmel et al. 2018). This suppression boosts the performance of the suppressor (Sarmento, Lemos, Bleeker, et al. 2011; Sarmento, Lemos, Dias, et al. 2011) but also can boost the performance of nearby nonsuppressors residing on the same plant (Kant et al. 2004), and suppressors can downregulate defenses across a diverse range of hosts (Paulo et al. 2018). Suppression of plant defenses is a well-known trait among phytopathogens and is established via molecules, often referred to as “effectors,” secreted into the host during the early and later stages of the infection (reviewed in Jones et al. 2022). Data on the functionality of effectors across multiple hosts is still rare, but some host plants have evolved mechanisms to detect the activity of effectors and by-pass their suppression effectively, turning the effector into an elicitor of plant defenses (reviewed in Kallure et al. 2022). Although effectors are associated with defense suppression, herbivores that induce defenses may also possess them (Villarroel et al. 2016) and thus effector-mediated induction/suppression depends on the presence of effector targets in the host as well as the host's ability to detect and respond to the collective of secreted substances (Cui et al. 2022).

Tetranychus urticae is a generalist spider mite species (Helle and Sabelis 1985) and a model herbivore for studying plant-herbivore interactions (Blaazer et al. 2018). When feeding on tomato plants (Solanum lycopersicum), most of the *T. urticae* strains

studied induce JA- and SA-dependent plant defenses leading
to decreased herbivore performance and survival (e.g., Kant
et al. 2008; Sarmento, Lemos, Bleeker, et al. 2011; Sarmento,
Lemos, Dias, et al. 2011; Alba et al. 2015; Godinho et al. 2016).
As such, most of the populations of this species are believed
to be susceptible to the tomato JA and SA defenses that are
induced through feeding (susceptible-inducer populations).
However, some strains can resist the plant defenses they induce
(resistant-inducer populations, Kant et al. 2008). It was shown
that the *T. urticae* genome has multiple gene families impli-
cated in xenobiotic metabolism, whose magnitude of expres-
sion changes markedly when adapting to new environments
(Zhurov et al. 2014; Wybouw et al. 2015). This suggests that
traits related to metabolic resistance may be favored by natu-
ral selection during adaptation to a novel host (Van Leeuwen
and Dermauw 2016; Blaazer et al. 2018). Nevertheless, adapta-
tion to a novel host also appeared to improve the population's
ability to suppress defenses (susceptible-suppressor populations,
Wybouw et al. 2015). The ability to suppress tomato defenses
was found to exist in low frequencies in natural *T. urticae* pop-
ulations while in another species, *T. evansi*, it appeared to be a
fixed species trait (Kant et al. 2008; Sarmento, Lemos, Bleeker,
et al. 2011; Sarmento, Lemos, Dias, et al. 2011; Alba et al. 2015).
Even since the discovery of defense suppression as an alterna-
tive to xenobiotic resistance in mites, researchers have specu-
lated about the role of these traits during adaptation to novel
hosts and how they relate to herbivores having a generalist or
specialist life styles (Blaazer et al. 2018). Defense suppression
by spider mites has been attributed to salivary proteins called
effectors and especially effector family 84 (Tu84 for *T. urti-
cae* and Te84 for *T. evansi* in Villarroel et al. 2016; Schimmel
et al. 2017; Liu, Chafi, et al. 2020). Here we will refer to these as
“*effector 84*.” The expression of *effector 84* is plastic and changes
in response to the presence of con- and heterospecific competi-
tors (Schimmel et al. 2017), to light/dark conditions (Liu, Chafi,
et al. 2020) and differs across mite life stages and sexes (Liu,
Legarrea, et al. 2020).

Previous studies found that natural populations of spider mites
collected on nonsolanaceous host plants, such as European
spindle tree (Euonymus europea), deadnettle (Lamium album),
and castor oil (Ricinus communis), exhibit phenotypic diversity.
These populations predominantly comprise individuals that
induce and are susceptible to tomato defenses, while a smaller
portion suppresses these defenses, with occasional occurrences
of inducer and resistant individuals (Kant et al. 2008; Alba
et al. 2015). As such, in this study, we asked if such diversity
can be found in natural populations living on field tomatoes
grown using organic or biological farming practices, as it was
shown that nonsuppressor mite strains from bean can evolve
defense suppression on tomato after 30 generations (Wybouw
et al. 2015). Hence, we aimed to understand if mites naturally
occurring on field-grown tomatoes display this trait as well,
and if the magnitude of expression of the *effector 84* correlates
with the level of defense suppression. We hypothesized that (i)
spider mites residing on tomato in the field will be adapted to
the defenses of these plants and thus harbor primarily defense-
resistant individuals, individuals that can suppress defenses, or
a mixture, (ii) defense suppression correlates with the expres-
sion of *effector 84*, and (iii) inducer and suppressor individuals
would comprise different phylogenetic clades on the basis of
their *effector 84* genotype. As such, we aimed to phenotype three mite populations collected from field-grown tomatoes for (i) their ability to resist or suppress tomato defenses, (ii) the correlation between *effector 84* expression and the magnitude of induced defenses, and (iii) a phylogenetic analysis of *effector 84*.

For this, we compared three *T. urticae* populations sampled from tomato at three field sites and also an outbred population created from these via controlled crosses. We then assessed their fecundity on tomato plants with (wildtype, WT) and without (*def-1*) inducible JA defenses, as well as the magnitude of JA defenses they induce in WT plants, to discriminate between resistant types (high induction and high performance) and those that can suppress defenses (low induction, high performance). We then aligned these data with data on variation in mitochondrial DNA cytochrome oxidase I (COI) and *effector 84* (which can suppress JA defenses), in order to compare genetic diversity patterns among mite lines that induce or suppress defenses. We found that suppression is the dominant phenotype in mite populations collected from field-grown tomatoes and that inducers and suppressors predominantly cluster in distinct *effector 84* clades.

# 2 | Material and Methods

## 2.1 | Plants

Tomato plants (*Solanum lycopersicum* L.), of the varieties Castlemart (both *WT* and *defenseless-1*, *def-1*) and Moneymaker, and bean plants (*Phaseolus vulgaris* L. cv Speedy) were germinated and grown (soil—Siro interior, Siro, Mira, Portugal) in a climatic chamber (photoperiod of 16:8 h, 25°C:18°C, day: night, 50%-60% relative humidity (RH)) for 28 and 10 days, respectively. On *def-1* tomato plants, JA defenses cannot be induced (Bergey et al. 1996; Howe et al. 1996; Li et al. 2002), making them a great tool to assess the effect of JA defenses on spider mites. Experiments were carried out in a climatic chamber (photoperiod of 16:8 h, 23.5°C ± 2°C, 70% RH), to which *WT* and *def-1* tomato plants (cv Castlemart) were transferred 1 day before the beginning of the experiment.

## 2.2 | Spider Mites

*Tetranychus urticae* was sampled in the field at three locations, from here on referred to as three mite populations (ALP, DEF, and MON). The populations were collected in tomato organic farms in Portugal in 2017 (more detailed information on collection site, time of the year, and plant variety can be found in Godinho et al. 2020) and were subject to a heat shock treatment for 42 days to remove endosymbionts such as *Wolbachia* (Godinho et al. 2020). These symbiont-free populations were then used to create an outbred laboratory population (Outbred) by performing interpopulation, two-way (i.e., female 1×male 2 and female 2×male 1) controlled crosses to normalize overrepresentation of genotypes. The outbred population started with more than 100 individuals (> 50 males and > 50 females) of each cross combination (Godinho et al. 2020). As such, since the outbred population represents a genetic mixture of the original populations due to interbreeding, studying this population allows for a better understanding of how the introduction of new

genetic combinations through interbreeding influences traits expression and genetic diversity.

All field and outbred populations were reared on detached tomato leaves (cv Moneymaker) with the petioles submerged in water.

All populations were maintained in a climatized room (photo-period of 16:8 h, 23.5°C ± 2°C), inside 4-L boxes with a mesh lid to allow airflow.

### 2.2.1 | Benchmark Populations and Biomarker

*Tetranychus urticae* Santpoort-2 ("KMB" in Kant et al. 2008, "Santpoort-2" in Alba et al. 2015) was used as defense-inducer benchmark. For the defense-suppressor benchmark, *T. evansi* Baker & Pritchard Viçosa-1 (Alba et al. 2015) was used, due to its inherent characteristic to consistently suppress tomato plant's defenses, which presents a marked contrast to the general scarcity of fully suppressed populations within *T. urticae* (up to this study), offering a valuable reference point for evaluating and understanding defense manipulation mechanisms among spider mite species. These populations have been widely used in studies of tomato-mite interactions (e.g., Sarmento, Lemos, Bleeker, et al. 2011; Sarmento, Lemos, Dias, et al. 2011; Villarroel et al. 2016; Schimmel et al. 2017; Liu, Chafi, et al. 2020; Liu, Legarrea, et al. 2020) and are well-characterized inducer and suppressor populations, respectively (Alba et al. 2015). This inherent characteristic of *T. evansi* to consistently suppress tomato plant defenses presents a marked contrast to the general scarcity of fully suppressed populations within *T. urticae* (up to this study). In the absence of a good benchmark or reference control for evaluating the degree of tomato defense suppression within *T. urticae* species, we used *T. evansi* Baker & Pritchard Viçosa-1. The defense-inducer benchmark was reared on detached bean plants (cv. Speedy), and the defense-suppressor benchmark was reared on detached tomato leaves (cv Castlemart). Plants infested with these benchmark populations and uninfested plants were used as controls across all assays.

*Tetranychus urticae* Santpoort-2 can also be used as a biomarker of JA-induced defenses since this population is susceptible to JA defenses (Kant et al. 2008). When on a plant preinfested with an inducer mite, the fecundity of this biomarker is lower than on an uninfested plant or than on a plant preinfested by a suppressor population (Kant et al. 2008; Sarmento, Lemos, Bleeker, et al. 2011; Alba et al. 2015). As such, this is a perfect biomarker to disentangle between resistant (i.e., inducer/resistant) and suppressor strains.

## 2.3 | Fecundity of *T. urticae* From Field-Collected Populations on WT and Def-1

We assessed the fecundity of each of the *T. urticae* populations when JA defenses are induced (using WT) and in the absence of these defenses (using *def-1*). To this end, 30 mated females (15 ± 1 day old) from each *T. evansi* and *T. urticae* population were placed on the same, nonterminal leaflet of a fully expanded leaf of whole WT or *def-1* tomato plants (Day 0, Figure S1). Mite
<table><caption>**TABLE 1** | Collection records of the *T. urticae* populations used for *COI* and *effector 84* sequencing.</caption><thead><tr><th>Population</th><th>Original population</th><th>Laboratory host</th><th>Field host</th><th>Location</th><th>Year</th><th>References</th></tr></thead><tbody><tr><td>ALP</td><td>ALP</td><td>Solanum lycopersicum (cv. Moneymaker)</td><td>Solanum lycopersicum</td><td>Alpiarça, Portugal</td><td>2017</td><td>Godinho et al. (2020)</td></tr><tr><td>DEF</td><td>DEF</td><td>Solanum lycopersicum (cv. Moneymaker)</td><td>Solanum lycopersicum</td><td>Lisboa, Portugal</td><td>2017</td><td>Godinho et al. (2020)</td></tr><tr><td>MON</td><td>MON</td><td>Solanum lycopersicum (cv. Moneymaker)</td><td>Solanum lycopersicum</td><td>Montemoro-Novo, Portugal</td><td>2017</td><td>Godinho et al. (2020)</td></tr><tr><td>Outbred</td><td>Outbred</td><td>Solanum lycopersicum (cv. Moneymaker)</td><td>Laboratory created population</td><td>NA</td><td>2018</td><td>Godinho et al. (2020)</td></tr><tr><td>Santpoort-1<sup>a</sup></td><td>KMT</td><td>Solanum lycopersicum (cv. Castlemart)</td><td>Euonymus europea</td><td>Santpoort, Netherlands</td><td>2001</td><td>Kant et al. (2008)/NCBI bioproject PRJNA78687</td></tr><tr><td>Santpoort-2<sup>b</sup></td><td>KMB</td><td>Phaseolus vulgaris, cv. Speedy</td><td>Euonymus europea</td><td>Santpoort, Netherlands</td><td>2001</td><td>Kant et al. (2008)</td></tr><tr><td>SN<sup>a</sup></td><td>NA</td><td>Solanum lycopersicum (cv. Castlemart)</td><td>Solanum nigrum</td><td>Málaga, Spain</td><td>2010</td><td>Sato et al. (2014)</td></tr><tr><td>London_NL<sup>a</sup></td><td>London</td><td>Phaseolus vulgaris, cv. Speedy</td><td>NI</td><td>Ontario, Canada</td><td>NI</td><td>Grbić et al. (2011)</td></tr></tbody></table>

Abbreviations: NA, nonapplicable; NI, no information available.

and *block* as a random variable. The models included the benchmark controls.

When significant differences were found, multiple comparisons were performed using estimated marginal means (means, emmeans package) (Lenth 2022), and the *p* values corrected using the false discovery rate (FDR) method (α=0.05) (Benjamini and Hochberg 1995). Cytochrome oxidase 1 (*COI*) and *effector 84* sequencing and assembly.

The genomic DNA (gDNA) of single adult females of the field-grown and laboratory populations (Table 1) was extracted by first crushing the specimens, previously frozen at -80°C, with a plastic pestle and adding 100 µL of 5% Chelex solution (Chelex 100 sodium form, Sigma-Aldrich, St. Louis, USA) and 5 µL proteinase-K (20 mg/mL). The samples were incubated at 56°C for 60 min, followed by denaturation at 95°C for 8 min (Walsh et al. 1991). Amplification of *COI* (seven to 14 separate individuals per population) and *effector 84* (eight to 13 separate individuals per population; see primers in Table S1) was performed using endpoint PCR and the presence of PCR product was confirmed on ethidium bromide-stained agarose gels. The remaining PCR products (1× diluted) were sent to Eurofins Genomics for Sanger sequencing.

The sequences were assembled and edited (i.e., sequence peaks were confirmed and corrected when needed) in SeqMan Pro 14 (version 14.1.0 (118), DNASTAR Lasergene 14, Madison, Wisconsin, United States) and aligned using MUSCLE (Edgar 2004) in MEGA (version 11, Tamura et al. 2021). The

*COI* alignment consisted of 115 sequences (385 bp), from which 35 were from GenBank (34 of *T. urticae* and one of *T. evansi* as an outgroup). From these, haplotypes were obtained using the DNAcollapser tool of Fabox (Villesen 2007, Table S3).

The reference *effector 84* gDNA sequences can be found in OrcAE (Sterck et al. 2012) as tetur01g01000 (*T. urticae* London and *T. urticae* Montpellier genome). The allele and protein variants obtained for *effector 84* can be found in Table S2. The ORF in the *effector 84* gDNA spans two exons: the first of 42 nucleotides and the second of 697 nucleotides separated by an intron 85 nucleotides long. We amplified 635 nucleotides from the second exon (see Figure S2). The *effector 84* alignment consisted of 103 sequences (635 bp, which, based on the gene model of Villaroel et al. 2016, corresponds to nucleotides 55 to 690), from which three were from GenBank (two of *T. urticae* and one of *T. evansi* as an outgroup—the latter with 637 bp). Note that since female mites are diploid, we sometimes obtained two haplotypes per individual. To resolve this, we implemented a Bayesian statistical method for phasing both haplotypes using PHASE v2.1 (Stephens et al. 2001) in DNAsp v5 (Librado and Rozas 2009).

## 2.7 | Sequence Analysis

For *effector 84*, DNA polymorphisms were analyzed using DNASP 4.0 (Rozas et al. 2003). As a measure of DNA polymorphism within populations, five parameters were estimated: the number of polymorphic/segregating sites (*S*), the total number

<footnote><sup>a</sup>Defense-inducer benchmark and biomarker strain.</footnote>

<footnote><sup>b</sup>Well documented populations in literature, which serve, in the present study, as a benchmark. more informations about this populations can be found in Kant et al. (2008), Sato et al. (2014) and Grbić et al. (2011), respectively.</footnote>
of mutations (*Eta*), the number of haplotypes (*h*), the haplotype diversity (*H*<sub>d</sub>, a measure of the frequencies and number of haplotypes among individuals, varying between 0 and 1, Nei 1987), and the nucleotide diversity (π, average weighted sequence divergence between haplotypes, varying between 0 for no divergence to 10% for very deep divergences, Tajima 1983). Note that this analysis was performed for the complete sequence dataset, but also for individual populations (i.e., ALP, DEF, MON, outbred, London_NL).

## 2.8 | Phylogenetic Analysis

We used PartitionFinder2 (Lan fear et al. 2016) and ModelFinder (Kalyaanamoorthy et al. 2017) to select the best partition arrangement and the most suitable evolutionary substitution model (COI: HKY+F+I for subset1 = 1-385\3 3-385\3, and HKY+F for subset2 = 2-385\3; *Tu84*: GTR+F+I+G4 for subset1 = 1-638\3 2-638\3 3-638\3). Bayesian Inference phylogenies were inferred using MrBayes 3.2.6 (Ronquist et al. 2012) (two parallel runs, 20 million generations), in which the initial 25% of sampled data were discarded as burn-in. All the analyses were run using the PhyloSuite version 1.2.2 desktop platform (Zhang et al. 2020). With the trees obtained from MrBayes, we computed 70% majority-rule consensus trees using SumTrees using SumTrees (Sukumaran and Holder 2010) from the Dendropy library (version 4.1.0, Sukumaran and Holder 2010).

*Tu84* sequences were translated following the gene model of Villaroel et al. (2016). For the protein analysis, the best phylogenetic model was estimated in MEGA 11. Then, a maximum likelihood phylogenetic analysis with 1000 bootstraps, using the LG+G model with gamma shape = 0.67, was performed, and a consensus tree with bootstraps higher than 50% was obtained using MEGA. The phylogenetic trees were edited in iTOL version 6.5.8 (Letunic and Bork 2021).

## 2.9 | Accession Numbers

The sequence data from this article are available at NCBI website (https://www.ncbi.nlm.nih.gov) and can be found under the following accession numbers: *Tu84*, OQ472023–OQ472057; *COI*, OQ510005–OQ510008.

# 3 | Results

## 3.1 | Fecundity of *T. urticae* From Field-Collected Populations on WT and *Def-1*

The fecundity of the field and outbred populations of *T. urticae* were not significantly different in plants either with or without JA defenses (WT, population: $\chi_3^2=0.910$, *p*=0.823, Figure 1A; *def-1*, population: $\chi_3^2=5.211$, *p*=0.157, Figure 1B), with all having a similar fecundity on WT and *def-1* plants. Our benchmark controls followed a typical pattern of inducer and suppressor strains, respectively, with Santpoort-2 having a lower fecundity on WT than Viçosa-1, (*p*<0.001), and both populations having similar fecundities on *def-1* (*p*=0.780).

## 3.2 | Induction of Tomato Defense Genes by the Field-Collected Spider Mites and Expression Levels of Their Effector 84

The normalized transcript levels of *effector 84* were different across populations (*population*: $\chi_5^2=85.729$, *p*<0.001, Figure 1C). Post hoc comparisons revealed that these differences are due to high levels of expression of this gene in *T. evansi Viçosa-1*, which are 50–150 times higher than for *T. urticae*. All *T. urticae* populations, including the defense-inducer benchmark, had similar transcript levels of *effector 84*.

Four days post infestation (4 dpi), the normalized transcript levels of JA and SA marker genes were different among populations (*population*: WIPI-IIC: $\chi_6^2=49.805$, *p*<0.001; WIPI-IIf: $\chi_6^2=52.733$, *p*<0.001; PR-1a: $\chi_6^2=51.279$, *p*<0.001; Figure 1D–F). Transcript levels in leaflets infested with the defense-suppressor benchmark were lower than the ones infested with the defense-inducer benchmark. Transcript accumulation levels for WIPI-IIC and PR-1a in plants infested with the field-collected or outbred populations were similar to the defense-suppressor benchmark and some were even similar to uninfested plants (Outbred, ALP and MON for WIPI-IIC and PR-1a). The transcript levels of WIPI-IIf in leaflets infested with MON were as high as for leaflets infested with the defense-inducer benchmark.

## 3.3 | Fecundity of the Biomarker Strain on Leaflets That Had Been Preinfested by Mites From Field-Collected Populations

The population preinfesting the plant had a significant effect on the fecundity of the biomarker (*population*: $\chi_6^2=23.843$, *p*<0.001; Figure 2A). Post hoc comparisons revealed that the biomarker had higher fecundity on plants preinfested with each of the four *T. urticae* populations tested (Outbred, ALP, DEF and MON; Figure 2A). However, there was no difference in fecundity on uninfested plants, or plants previously infested with the suppressor or the inducer benchmark.

After introducing three adult females of the biomarker strain, the transcript levels of all defense-related genes changed (*population*\*time point: WIPI-IIC: $\chi_6^2=35.944$, *p*<0.001; WIPI-IIf: $\chi_6^2=49.365$, *p*<0.001; PR-1a: $\chi_6^2=13.929$, *p*=0.030; Figure 2B–E). Post hoc comparisons revealed that for all genes, except for WIPI-IIC, transcript levels of uninfested plants were higher at 6 dpi compared to 4 dpi. However, for plants preinfested with the field-collected, outbred and benchmark populations, transcript levels mostly remained similar to, or were lower than, 4 dpi (lower at 6 dpi for (i) defense-inducer benchmark, DEF and MON for WIPI-IIC; (ii) defense-suppressor, Outbred and MON for WIPI-IIf; (iii) Outbred for PR-1a).

## 3.4 | Phylogenetic Analysis of *COI* and *Effector 84*

Twenty mitochondrial haplotypes (M1–M20) were found based on the *COI* sequences. All the populations sequenced, except Santpoort-1, belong to the same lineage of *T. urticae* (Figure S3).
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Phenotypic characterization of the field-collected and the outbred populations. We measured fecundity of each population in the (A) presence (WT, white) and (B) absence (*def*-1, gray) of JA defenses after 4 days of infestation, and transcript levels of (C) salivary *effector 84*, JA-related defenses (D) *WIPI-IIc*, (E) *WIPI-IIf*, and SA-related defenses (F) *PR-1a* at 4 dpi. Each circle represents individual data points (n = 8–11). Different lowercase letters denote statistical differences between populations according to multicomparison analysis performed using estimated marginal means. The benchmarks are denoted as “I” for the defense-inducer and “S” for the defense-suppressor, and “U” for uninfested plants, and the outbred population is denoted as “O”.</figcaption></figure>

For the *effector 84* (Figure 3), 38 alleles (N1–N38) were identified. The haplotype diversity (*H*<sub>d</sub>) and nucleotide diversity (π) of all sequences were 0.910 and 0.017, respectively (Table 2). From the populations harboring different alleles, DEF and the outbred populations were the ones with higher *H*<sub>d</sub> and π (Table 2).

All the individuals sequenced from the defense-inducer popu-
lation (Santpoort-2) had one allele (N25), sharing the same phy-
logenetic clade with Santpoort-1 (also with one allele, N15) and
London_NL (with seven alleles). These populations shared the
same clade with the reference alleles (N37 and N38). The popula-
tion collected in Spain, SN (allele N26), and the Portuguese field
and outbred populations were in a separate clade, supported by
a bootstrap of 76%. The allele most shared between individuals
in the field and, consequently in the outbred population, was N2
(representes 53% for ALP, 33% for DEF, 78% for MON, and 37%
for Outbred of all allelic variations). We found multiple alleles
within ALP, DEF and MON (5, 8, and 3 alleles, respectively) with
some being singletons (i.e., alleles represented by a single se-
quence). Twelve alleles were found in the outbred population, of
which 3 were also present in the field-collected populations. The
38 alleles were found to encode 26 different proteins (P1–P26).

The same arrangement of the phylogenetic tree for the nucleo-
tide clades was observed for the protein sequences (Figure S3).

4 | Discussion

In this study, we showed that spider mites collected from popu-
lations naturally occurring on tomato plants in the field can be
classified as suppressors of tomato defenses. The field-collected
and outbred populations had similar fecundity on WT and *def-1*,
and they suppressed the induction of tomato defense genes by
the defense-inducer benchmark strain. Also, the field-collected
mites and the outbred population promoted the fecundity of the
biomarker strain. Expression levels of *effector 84*, associated
with defense suppression by mites, were similar across *T. urticae*
lines that induce or suppress tomato defenses. We speculate that
such allelic diversity may be subject to natural selection when
these mites colonize a novel host plant and promote traits that
counter plant resistance, such as defense suppression, thereby
facilitating adaptation.

Our observation aligns well with predictions that follow pre-
vious research. Previous research showed that spider mites
<figure><img src="image_4.png" /><figcaption>**FIGURE 2** | Effect of preinfestation on the biomarker. We measured (A) fecundity at 6 dpi and normalized gene expression of JA-related genes (B) *WIPI-IIc*, (C) *WIPI-IIf*, and SA-related defenses (D) *PR-1a* at 6 dpi. Each circle represents individual data points (n = 5–11). Different lowercase letters denote statistical differences between populations according to multicomparison analysis performed using estimated marginal means. The benchmarks are denoted as “I” for the defense-inducer and “S” for the defense-suppressor, and “U” for uninfested plants, and the outbred population is denoted as “O.”</figcaption></figure>

collected in the field from nonsolanaceous hosts, notably
spindle tree and deadnettle, predominantly consist of indi-
viduals that are maladapted to tomato (as a novel host) (Kant
et al. 2008; Alba et al. 2015). Most of these mites were found
to strongly induce JA defenses in tomato plants and to be sus-
ceptible to these defenses (Kant et al. 2008; Alba et al. 2015).
However, in these populations, individuals that can suppress
or resist tomato defenses were occasionally found, albeit at low
frequencies (Kant et al. 2008; Villacis-Perez et al. 2022). This
goes in line with what was previously shown (e.g., Magalhães
et al. 2007), as it suggests that such natural spider mite pop-
ulations may contain sufficient standing variation to adapt
to a novel host like tomato. This was confirmed by Wybouw
et al. (2015) where it was shown that spider mites from bean
could adapt to tomato and that this coincided with the emer-
gence of the ability to suppress tomato defenses. Together
these observations suggest that spider mites living on a nonto-
mato host (spindle tree, deadnettle, and castor oil) will have a
lower fitness once on tomato but will adapt to it when natural
selection favors the low-frequency traits that allow these mites
to resist or suppress tomato defenses.

4.1 | Defense Suppression of Tomato Defenses Is Common in *T. urticae* Collected From Field-Grown Tomatoes

The field populations showed similar oviposition on both hosts,
suggesting that they can cope with induced JA defenses. As ob-
served previously, defense-inducer mites have lower oviposition
on WT (Figure 1A) than suppressor mites (Kant et al. 2008;
Alba et al. 2015), while in *def-1* (Figure 1B) oviposition is sim-
ilar for both populations (Li et al. 2002; Kant et al. 2008; Alba
et al. 2015). However, similar levels of oviposition on both WT
and *def-1* can be a sign of either metabolic resistance against
plant defenses induced by the spider mites or defense suppres-
sion (Kant et al. 2008).

We observed that all mites collected from populations of field-
grown tomato plants tested could suppress tomato defenses to
varying levels and that this trait was maintained in an outbred
population. For all tomato defense genes measured, normal-
ized transcript accumulation was higher in plants infested by
the defense-inducer benchmark and lower for the remaining
<figure><img src="image_5.png" /><figcaption>**FIGURE 3** | Phylogenetic relationships of *T. urticae* effector 84 alleles (partial ORF sequence, 655 bp; 658 bp for *T. evansi* outgroup, N36). Alleles belonging to the populations phenotypically characterized in this study are highlighted in bold. Alleles from defense suppressor populations are highlighted in purple. Populations for which only one allele was found are referred to in front of the respective allele. Reference sequences have their accession numbers in brackets. For allelic codes see Table 1 (or Table S2 for more detail) and for SNPs see Figure S4. ¹Defense-inducer benchmark and biomarker strain.</figcaption></figure>

populations (Figure 1D–F), sometimes to levels of uninfested plants (for *WIPI-IIc* and *PR-1a*). However, some variation in levels of defenses was observed, with some populations not differing from the defense-inducer benchmark for some genes (i.e., DEF for *WIPI-IIc* and *PR-1a*, and MON for *WIPI-IIf*). Indeed, plant marker gene variability can introduce genetic diversity within supposedly homogeneous populations, being crucial to assess the effect of induction and suppression in mite fecundity, though a biomarker strain. Despite this variation, the overall results indicated that the field populations upregulate defenses to much lower levels than the defense-inducer benchmark; whereas, in turn, the inducer benchmark did not upregulate defenses anymore in plants preinfested with a field strain (Figure 2B–D). These data indicate that field-collected mites can suppress inducible plant defenses.

## 4.2 | Spider Mites Collected From Field-Grown Tomatoes Promote Fecundity and Suppress Defense Genes Induced by the Biomarker Strain

Previous results on the effect of suppression on the fecundity of a biomarker strain suggested that defense suppression has

a persistent effect after the removal of the inducer/suppressor mites (Kant et al. 2008; Alba et al. 2015; Godinho et al. 2016; de Oliveira et al. 2016; Schimmel et al. 2017, 2018). However, whether the lasting effect of suppression also includes sustained suppression of defense gene expression was not previously assessed (but see Teodoro-Paulo et al. 2023). The results here suggest this to be the case. Similarly, it had been found previously that suppression of defense genes by the *T. urticae* sister species *T. evansi* is retained for at least 4 days (de Oliveira et al. 2019). It is possible that, because mite feeding and oviposition occur in the same location, a lasting effect of suppression by the adults provides their offspring with better food (Bruessow et al. 2010). As such, a longer-lasting effect of defense suppression, especially for SA marker genes, could provide a beneficial environment for offspring development. Investigating this further will give insights into the mechanism (why it is lasting), function, and ecological costs (who benefits from it—offspring and/or competitors). This may help to formulate testable hypotheses regarding mite life history, community composition, and adaptation.

In line with the defense gene induction/suppression pattern, we found that preinfestation with mites collected from field-grown tomatoes and the outbred population increased the fecundity of the biomarker strain (Figure 2A). Our results indicate that, although the magnitude of suppression was not uniform across the mite lines we tested, the effect on the fecundity of the biomarker was similar. In this experiment, the effect of the defense-inducer and defense-suppressor benchmarks on the biomarker strain were not significant, probably due to a large amount of variation in the uninfested control (Figure 2A). Previous studies observed clearer effects of the induction and suppression benchmarks on the biomarker strain (Kant et al. 2008; Alba et al. 2015; Godinho et al. 2016). However, other studies also failed to observe significant effects using this assay despite seeing the expected trends and this may be due to power (sample size) and timing (de Oliveira et al. 2016; Schimmel et al. 2017, 2018; Blaazer et al. 2018).

## 4.3 | There Is Significant Genetic Variation in Effector 84 Across *T. urticae* Populations

Despite the variation in tomato defense suppression among the field-collected spider mites and outbred population, the expression levels of the salivary *effector 84* were similar to those of the defense-inducer benchmark (Figure 1C). *effector 84* from both *T. urticae* and *T. evansi* downregulate defense gene expression in *N. benthamiana* upon transient overexpression (Villarroel et al. 2016) and it can suppress elicitor-induced cell death in both *N. benthamiana* and tomato (Cui et al. 2022). Moreover, *effector 84* expression in *T. urticae* correlates negatively with the magnitude of induced defenses in tomato (Schimmel et al. 2017; Liu, Chafi, et al. 2020; Liu, Legarrea, et al. 2020). However, given our *effector 84* expression data, it is certain that this effector is not the only determinant of defense suppression by *T. urticae*. Probably, induction/suppression results from a complex interplay between mite effectors and elicitors (Cui et al. 2022; Villarroel et al. 2016; Iida et al. 2019; Jonckheere et al. 2016; Jonckheere et al. 2018), and sequence and expression variation therein. To this end, we explored effector sequence variation at the nucleotide and amino
<table><caption>TABLE 2 | COI-haplotype, *effector 84* alleles and protein sequences codes, and measures of DNA polymorphism within populations for *effector 84*.</caption><thead><tr><th>Population</th><th>COI-haplotype</th><th>Effector 84 alleles</th><th>Protein</th><th>S</th><th>Eta</th><th>h</th><th>H<sub>d</sub> (±SD)</th><th>π% (±SD)</th></tr></thead><tbody><tr><td>All sequences</td><td>M1-M20</td><td>N1-N38</td><td>P1-P26</td><td>155</td><td>162</td><td>38</td><td>0.910<br>(±0.018)</td><td>0.017<br>(±0.004)</td></tr><tr><td>ALP</td><td>M6</td><td>N1-N6</td><td>P1-P4</td><td>4</td><td>4</td><td>6</td><td>0.718<br>(±0.128)</td><td>0.002<br>(±0.001)</td></tr><tr><td>DEF</td><td>M6</td><td>N2<br>N6-N14</td><td>P1<br>P4-P8</td><td>9</td><td>9</td><td>10</td><td>0.895<br>(±0.070)</td><td>0.006<br>(±0.001)</td></tr><tr><td>MON</td><td>M6</td><td>N2<br>N23<br>N24</td><td>P1<br>P16<br>P17</td><td>9</td><td>9</td><td>3</td><td>0.464<br>(±0.200)</td><td>0.005<br>(±0.002)</td></tr><tr><td>Outbred</td><td>M6</td><td>N1<br>N2<br>N23<br>N27-N35</td><td>P1<br>P16<br>P17-P23</td><td>9</td><td>9</td><td>12</td><td>0.890<br>(±0.073)</td><td>0.006<br>(±0.001)</td></tr><tr><td>Santpoort-1</td><td>M11</td><td>N15</td><td>P9</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Santpoort-2<sup>a</sup></td><td>M13</td><td>N25</td><td>P18</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>SN</td><td>M7</td><td>N26</td><td>P19</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>London_NL</td><td>M6</td><td>N16-N22</td><td>P10-P15</td><td>7</td><td>7</td><td>7</td><td>0.792<br>(±0.089)</td><td>0.003<br>(±0.001)</td></tr></tbody></table>

Note: The number of polymorphic/segregating sites (S), the total number of mutations (Eta), the number of haplotypes (h), the haplotype diversity (Hd, Nei 1987), and the nucleotide diversity (π, Tajima 1983).

Abbreviation: NA, nonapplicable.

acid level and performed a phylogenetic analysis to search for relationships between sequence variation and phenotype.

Unlike İnak et al. (2019), which found two mitochondrial lineages of *T. urticae*, our analysis revealed three mitochondrial lineages (Figure S3). This might be explained by the inclusion of several COI-haplotypes not considered in the previous phylogeny, such as the Portuguese and Dutch populations, and the use of different phylogenetic methodologies. We found that the three field-collected populations, and hence the outbred population, are of the same COI-haplotype as London_NL (haplotype M6, lineage I, Figure S3). These populations shared the same lineage (lineage I) as the Spanish population (SN, haplotype M7) and the defense-inducer benchmark (Santpoort-2, haplotype M13), although these two were more closely related. Santpoort-1 belongs to a more distinct lineage (lineage II, haplotype M11, Figure S3). Although the field-collected populations shared the same COI-haplotype (at least in the fragment sequenced), they are from different origins and have different nuclear alleles (*effector 84*, discussed below), suggesting that these populations are genetically different.

Across all the mite lines we tested, we found 38 different alleles for *effector 84*, and these translated into 26 different proteins. We do not know if some of the amino acid variations we observed affect *effector 84* functionality or structure. The alpha-fold structure prediction of *effector 84* has low confidence (see Uniprot (UniProt Consortium 2019): T1JPW1) hampering accurate assessment to estimate the effect of single amino acid changes on structure and function using bioinformatics alone, and thus functional studies as in Villarroel et al. (2016) or Cui et al. (2022)

will be necessary to explore this further. Across the *T. urticae* lines and populations we sequenced, *effector 84* has a high haplotype diversity (0.910, Table 2) but a very low nucleotide diversity (0.017%, Table 2). Patterns similar to this global one were observed in several of the local populations (ALP, DEF, London). These patterns may be the result of the action of purifying selection and/or rapid population rebound following a drastic past demographic bottleneck (Grant and Bowen 1998). We identified 38 alleles across all the populations sequenced, with the field populations harboring 15 of these, and the outbred population 12 (of which, three were shared with the field populations). This suggests high genetic differentiation between the founders of the outbred population and the individuals analyzed here representing each of the three field populations. This high genetic differentiation by chance underscores the huge haplotype diversity in each of the three source populations. Nevertheless, the SNPs present in the most common allele, which is shared between field-collected and outbred populations, N2 (which codes for protein P1), could explain defense suppression in these populations.

## 4.4 | Inducer and Suppressor Mites Predominantly Cluster in Different *Effector 84* Clades

We observed that inducer and suppressor populations do have different alleles and proteins. However, these differences do not segregate completely into inducer and suppressor mites (Figure 3). Instead, the Spanish population, SN (allele N26, found in an alleged inducer population, Sato et al. 2014) clustered

<footnote><sup>a</sup>Defense-inducer benchmark and biomarker strain.</footnote>
with the field and outbred populations collected in Portugal (alleles N1–14, N23–24, and N27–35). Also, *T. urticae* Santpoort-1 (allele N15, characterized as a suppressor population in Kant et al. 2008) clustered with the defense-inducer benchmark, Santpoort-2, and with *T. urticae* London (allele from reference genome: N25 and alleles from London_NL: N16–22). This can be explained by various factors. First, the two clusters observed in the *effector 84* gene tree may represent different geographical origins of the populations, as those collected in Portugal (ALP, DEF, MON) and Spain (SN) clustered together. Second, this clustering could be related to host adaptation. The populations collected in Portugal and Spain were collected on *Solanum* hosts, while the others were collected from more distal hosts. It was previously observed that populations of *T. urticae* from bean can adapt via natural selection to tomato plants, increasing their metabolite detoxification potential and their ability to reduce the production of plant defense compounds (Wybouw et al. 2015). This suggests that defense suppression of tomato defenses may rapidly emerge as a result of the selection on tomato (Blaazer et al. 2018). Indeed, the field-collected *T. urticae* populations all had been obtained from tomato plants (in the same geographic area) and were maintained in the laboratory on tomato ever since (Godinho et al. 2020). Also, the creation of the outbred population was performed entirely on tomato plants. Hence adaptation to tomato may have acted on (low frequency) traits that, coincidentally or not, allow for suppressing tomato defenses. While selection for direct resistance to tomato defenses may be characterized by rapid selection for detoxification genes (Dermauw et al. 2013), selection for suppression may depend on selection for potent effector genes. This could have implications for agricultural systems (especially in monoculture scenarios) where the ecological costs of suppression may be relatively low due to a lack of community diversity. Although R-gene resistance breeding in plants (Keith and Mitchell-Olds 2013) may provide a temporary solution, the mere existence of multiple effector alleles in all the populations and strains suggests that such plant resistance can be broken easily (Pilet-Nayel et al. 2017). We advocate that searching for effector targets to design breeding schemes to remove them or to make them invulnerable to effector manipulation (Zaidi et al. 2018) could offer an alternative approach, potentially complementing sustainable crop protection measures.

## 4.5 | To Resist or to Suppress

Our results do raise important questions regarding the outcome of adaptive processes of herbivores that have to cope with the novel defenses of novel hosts such as mites on tomato.

First, the question of what determines whether suppression or resistance is favored by natural selection. This will probably strongly depend on the standing variation in the founder population, which, in turn, is determined by selection (time), drift, and gene flow on the previous host. Subsequently, it will also be determined by the physiological costs, determined by the ease by which mites can degrade, modify, secrete, or sequester defensive metabolites will be different across different plant hosts (Yang et al. 2001), and the ecological costs of the novel host may be. Also, the efficiency of an herbivore's effectors on the novel host, determined by the absence/presence of suitable targets (susceptibility proteins) as well as of R-genes, and the effector variants

present in the population will determine the success of suppression as an adaptive strategy (Blaazer et al. 2018). Furthermore, also the presence of competitors will influence whether resistance may be favored over suppression because the latter is a common good (Kant et al. 2008; Sarmento, Lemos, Bleeker, et al. 2011; Alba et al. 2015) whereas the first is not. Finally, it will be determined by the ability of the local herbivore population to mitigate the ecological costs, as the sister species *T. evansi* actively excludes its competitor *T. urticae* from suppressed plant material via thick webbing production (Sarmento, Lemos, Dias, et al. 2011), suppression localization (Schimmel et al. 2017), and reproductive interference (Sato et al. 2014).

Second, there is the question of whether resistance and suppression are complementary traits that can coexist in populations adapted, or whether one will exclude the other, or whether one will follow the other. Also, this will probably be determined by the physiology and secondary chemistry of the novel host as well as gene flow between mite populations in the vicinity of different hosts. It was suggested that suppression of defenses via effectors may promote fitness less than direct resistance (i.e., may be less effective in counteracting plant defenses in absolute terms, yet may operate on a wider host range, Kant et al. 2015). In that view, suppression may be favored by selection first (Wybouw et al. 2015) and then later be replaced by resistance to mitigate the ecological costs of suppression (Blaazer et al. 2018). However, this is not in line with the observation that populations from all over the world of the tomato specialist *T. evansi* cope with tomato and other hosts (Paulo et al. 2018) via suppression (Knegt et al. 2020). Maybe this species lacks the necessary building blocks for evolving resistance, or its active exclusion of competitors from its feeding site (Sarmento, Lemos, Dias, et al. 2011; Schimmel et al. 2017; Sato et al. 2014) may buffer selection against suppression.

All these hypotheses need to be addressed in future research to gain a better understanding of how herbivore intraspecific variation, gene flow, host plant identity, and community composition determine the outcome of adaptive processes of herbivores on novel hosts, and the evolution of host races, for specialists and also for generalists living on a mosaic of plant environments.

## 5 | Conclusions and Perspectives

Our results show that the ability to suppress tomato defenses in *T. urticae* is common among mites living on tomato in the field. We argue that this trait may emerge in populations that have dispersed from an unrelated host, given that the traits needed for suppressing tomato defenses are included in its standing variation as had been shown previously (Kant et al. 2008; Alba et al. 2015; Wybouw et al. 2015). The question remains: What determines direct resistance to tomato defenses to emerge and how does this interact with suppression? Rapid adaptation of mites to plant resistance can be of great concern and especially impact monocultures in agriculture. Since traits related to induction/suppression and resistance/susceptibility to a novel host may be retained (at low frequencies) populations adapted to nontomato hosts (Kant et al. 2008; Villacis-Perez et al. 2022), it would now be informative to transfer our tomato lines to unrelated hosts to see whether a similar adaptive response can be observed under
these new conditions. We feel that studies regarding effector genetic and functional variation may reveal some of the mechanisms that drive plant-herbivore coevolution.

## Author Contributions

**Jéssica Teodoro-Paulo:** conceptualization (lead), data curation (lead), formal analysis (equal), funding acquisition (equal), investigation (lead), methodology (lead), visualization (lead), writing – original draft (lead), writing – review and editing (equal). **Carlos Fernandes:** data curation (equal), formal analysis (equal), methodology (equal), software (equal), supervision (equal), validation (equal), writing – review and editing (equal). **Lin Dong:** investigation (supporting), methodology (supporting). **Sara Magalhães:** conceptualization (supporting), funding acquisition (equal), resources (equal), writing – review and editing (supporting). **Alison B. Duncan:** conceptualization (equal), data curation (equal), formal analysis (supporting), supervision (equal), validation (equal), writing – review and editing (equal). **Juan M. Alba:** conceptualization (equal), data curation (equal), formal analysis (equal), methodology (equal), supervision (equal), validation (equal), visualization (equal), writing – review and editing (equal). **Merijn R. Kant:** conceptualization (equal), data curation (equal), funding acquisition (lead), methodology (equal), resources (lead), software (lead), supervision (lead), validation (equal), visualization (equal), writing – original draft (equal), writing – review and editing (equal).

## Acknowledgments

The authors would like to thank Merijn Kant's team at the University of Amsterdam, Alison Duncan's team at the University of Montpellier and Sara Magalhães's team at the University of Lisbon, for fruitful discussions throughout the project.

## Disclosure

Benefit-Sharing Statement: Benefits from this research accrue from the sharing of our data and results on public databases as described above.

## Ethics Statement

The authors have nothing to report.

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

Data are available in an open-access data repository: https://doi.org/10.5061/dryad.j6q573nq9. Unique haplotype data are deposited to NCBI Nucleotide Database (OQ472023–OQ472057, OQ510005–OQ510008).

## References

Agrawal, A. A., and R. Karban. 1999. "Why Induced Defenses May Be Favored Over Constitutive Strategies in Plants." *Ecology and Evolution of Inducible Defenses* 10: 000331745.

Alba, J. M., B. C. J. Schimmel, J. J. Glas, et al. 2015. "Spider Mites Suppress Tomato Defenses Downstream of Jasmonate and Salicylate Independently of Hormonal Crosstalk." *New Phytologist* 205, no. 2: 828–840.

Ali, J. G., and A. A. Agrawal. 2012. "Specialist Versus Generalist Insect Herbivores and Plant Defense." *Trends in Plant Science* 17: 293–302.

Bates, D., R. Kliegl, S. Vasishth, and H. Baayen. 2015. "Parsimonious Mixed Models." *arXiv Preprint* arXiv:1506.04967.

Benjamini, Y., and Y. Hochberg. 1995. "Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing." *Journal of the Royal Statistical Society: Series B: Methodological* 57: 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

Berenbaum, M. R. 1986. "Target Site Insensitivity in Insect-Plant Interactions." In *Molecular Aspects of Insect-Plant Associations*, 257–272. Springer US. https://doi.org/10.1007/978-1-4613-1865-1_7.

Bergey, D. R., G. A. Howe, and C. A. Ryan. 1996. "Polypeptide Signaling for Plant Defensive Genes Exhibits Analogies to Defense Signaling in Animals." *Proceedings of the National Academy of Sciences of the United States of America* 93: 12053–12058. https://doi.org/10.1073/pnas.93.22.12053.

Blaazer, C. J. H., E. Villacis-Perez, R. Chafi, T. Van Leeuwen, M. R. Kant, and B. C. J. Schimmel. 2018. "Why Do Herbivorous Mites Suppress Plant Defenses." *Frontiers in Plant Science* 9: 1057. https://doi.org/10.3389/fpls.2018.01057.

Bowers, M. D. 2022. "Sequestered Caterpillar Chemical Defenses: From "Disgusting Morsels" to Model Systems." In *Caterpillars in the Middle: Tritrophic Interactions in a Changing World*, edited by R. J. Marquis and S. Koptur, 165–192. Springer International Publishing. https://doi.org/10.1007/978-3-030-86688-4_6.

Brooks, M. E., K. Kristensen, K. J. van Benthem, et al. 2017. "glmmTMB Balances Speed and Flexibility Among Packages for Zero-Inflated Generalized Linear Mixed Modeling." *R Journal* 9: 378–400. https://doi.org/10.32614/rj-2017-066.

Bruessow, F., C. Gouhier-Darimont, A. Buchala, J.-P. Metraux, and P. Reymond. 2010. "Insect Eggs Suppress Plant Defence Against Chewing Herbivores." *Plant Journal* 62: 876–885.

Cianfrognna, J., A. Zangerl, and M. Berenbaum. 2002. "Dietary and Developmental Influences on Induced Detoxification in an Oligophage." *Journal of Chemical Ecology* 28: 1349–1364.

Cui, J.-R., X.-L. Bing, Y.-J. Tang, et al. 2022. "A Conserved Protein Disulfide Isomerase Enhances Plant Resistance Against Herbivores." *Plant Physiology* 191: 660–678. https://doi.org/10.1093/plphys/kiac489.

de Oliveira, E. F., A. Pallini, and A. Janssen. 2016. "Herbivores With Similar Feeding Modes Interact Through the Induction of Different Plant Responses." *Oecologia* 180: 1–10. https://doi.org/10.1007/s00442-015-3344-0.

de Oliveira, E. F., A. Pallini, and A. Janssen. 2019. "Herbivore Performance and Plant Defense After Sequential Attacks by Inducing and Suppressing Herbivores." *Insect Science* 26: 108–118. https://doi.org/10.1111/1744-7917.12499.

Dermauw, W., N. Wybouw, S. Rombauts, et al. 2013. "A Link Between Host Plant Adaptation and Pesticide Resistance in the Polyphagous Spider Mite *Tetranychus urticae*." *Proceedings of the National Academy of Sciences of the United States of America* 110: E113–E122.

Després, L., J.-P. David, and C. Gallet. 2007. "The Evolutionary Ecology of Insect Resistance to Plant Chemicals." *Trends in Ecology and Evolution* 22: 298–307. https://doi.org/10.1016/j.tree.2007.02.010.

Edgar, R. C. 2004. "MUSCLE: Multiple Sequence Alignment With High Accuracy and High Throughput." *Nucleic Acids Research* 32: 1792–1797. https://doi.org/10.1093/nar/gkh340.

Glazebrook, J. 2005. "Contrasting Mechanisms of Defense Against Biotrophic and Necrotrophic Pathogens." *Annual Review of Phytopathology* 43: 205.

Godinho, D. P., M. A. Cruz, M. C. De La Masseliere, et al. 2020. "Creating Outbred and Inbred Populations in Haplodiploids to Measure Adaptive Responses in the Laboratory." *Ecology and Evolution* 10: 7291–7305. https://doi.org/10.1002/ece3.6454.

Godinho, D. P., A. Janssen, T. Dias, C. Cruz, and S. Magalhães. 2016. "Down-Regulation of Plant Defence in a Resident Spider Mite Species
and Its Effect Upon Con- and Heterospecifics.” *Oecologia* 180: 161–167.
https://doi.org/10.1007/s00442-015-3434-z.

Grant, W., and B. W. Bowen. 1998. "Shallow Population Histories in Deep Evolutionary Lineages of Marine Fishes: Insights From Sardines and Anchovies and Lessons for Conservation." *Journal of Heredity* 89: 415–426.

Grbić, M., T. Van Leeuwen, R. M. Clark, et al. 2011. "The Genome of *Tetranychus urticae* Reveals Herbivorous Pest Adaptations." *Nature* 479: 487–492.

Groen, S. C., and N. K. Whiteman. 2022. "Ecology and Evolution of Secondary Compound Detoxification Systems in Caterpillars." In *Caterpillars in the Middle: Tritrophic Interactions in a Changing World*, edited by R. J. Marquis and S. Koptur, 115–163. Springer International Publishing. https://doi.org/10.1007/978-3-030-86688-4_5.

Heckel, D. G. 2014. "Insect Detoxification and Sequestration Strategies." *Annual Plant Reviews: Insect-Plant Interactions* 47: 77–114. https://doi.org/10.1002/9781118829783.ch3.

Helle, W., and M. W. Sabelis. 1985. *Spider Mites: Their Biology, Natural Enemies and Control*. Elsevier.

Howe, G. A., J. Lightner, J. Browse, and C. A. Ryan. 1996. "An Octadecanoid Pathway Mutant (JL5) of Tomato Is Compromised in Signaling for Defense Against Insect Attack." *Plant Cell* 8: 2067–2077.
https://doi.org/10.1105/tpc.8.11.2067.

Iida, J., Y. Desaki, K. Hata, et al. 2019. "Tetranins: New Putative Spider Mite Elicitors of Host Plant Defense." *New Phytologist* 224: 875–885.

İnak, E., Y. N. Alpkent, S. Çobanoğlu, W. Dermauw, and T. Van Leeuwen. 2019. "Resistance Incidence and Presence of Resistance Mutations in Populations of *Tetranychus urticae* From Vegetable Crops in Turkey." *Experimental and Applied Acarology* 78: 343–360. https://doi.org/10.1007/s10493-019-00398-w.

Jonckheere, W., W. Dermauw, V. Zhurov, et al. 2016. "The Salivary Protein Repertoire of the Polyphagous Spider Mite *Tetranychus urticae*: A Quest for Effectors." *Mol Cell Proteomics* 15, no. 12: 3594–3613.

Jonckheere, W., W. Dermauw, M. Khalighi, et al. 2018. "A Gene Family Coding for Salivary Proteins Shot of the Polyphagous Spider Mite *Tetranychus urticae* Exhibits Fast Host Dependent Transcriptional Plasticity." *Molecular Plant-Microbe Interactions* 31: 112–124. https://doi.org/10.1094/mpmi-06-17-0139-r.

Jones, A. C., G. W. Felton, and J. H. Tumlinson. 2022. "The Dual Function of Elicitors and Effectors From Insects: Reviewing the "Arms Race" Against Plant Defenses." *Plant Molecular Biology* 109: 427–445.
https://doi.org/10.1007/s11103-021-01203-2.

Kallure, G. S., A. Kumari, B. A. Shinde, and A. P. Giri. 2022. "Characterized Constituents of Insect Herbivore Oral Secretions and Their Influence on the Regulation of Plant Defenses." *Phytochemistry* 193: 113008.

Kaloshian, I., and L. L. Walling. 2005. "Hemipterans as Plant Pathogens." *Annual Review of Phytopathology* 43, no. 1: 491–521.

Kalyaanamoorthy, S., B. Q. Minh, T. K. F. Wong, A. von Haeseler, and L. S. Jermiin. 2017. "ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates." *Nature Methods* 14: 587–589. https://doi.org/10.1038/nmeth.4285.

Kant, M. R., K. Ament, M. W. Sabelis, M. A. Haring, and R. C. Schuurink. 2004. "Differential Timing of Spider Mite-Induced Direct and Indirect Defenses in Tomato Plants." *Plant Physiology* 135: 483–495. https://doi.org/10.1104/pp.103.038315.

Kant, M. R., M. W. Sabelis, M. A. Haring, and R. C. Schuurink. 2008. "Intraspecific Variation in a Generalist Herbivore Accounts for Differential Induction and Impact of Host Plant Defences." *Proceedings of the Royal Society B: Biological Sciences* 275: 443–452. https://doi.org/10.1098/rspb.2007.1277.

Kant, M., W. Jonckheere, B. Knegt, et al. 2015. "Mechanisms and Ecological Consequences of Plant Defence Induction and Suppression in Herbivore Communities." *Annals of Botany* 115: 1015–1051.

Karban, R., and J. H. Myers. 1989. "Induced Plant Responses to Herbivory." *Annual Review of Ecology, Evolution, and Systematics* 20: 331–348. https://doi.org/10.1146/annurev.es.20.110189.001555.

Keith, R., and T. Mitchell-Olds. 2013. "Genetic Variation for Resistance to Herbivores and Plant Pathogens: Hypotheses, Mechanisms and Evolutionary Implications." *Plant Pathology* 62: 122–132.

Knegt, B., T. T. Meijer, M. R. Kant, E. T. Kiers, and M. Egas. 2020. "Tetranychus Evansi Spider Mite Populations Suppress Tomato Defenses to Varying Degrees." *Ecology and Evolution* 10: 4375–4390. https://doi.org/10.1002/ece3.6204.

Lanfear, R., P. B. Frandsen, A. M. Wright, T. Senfeld, and B. Calcott. 2016. "PartitionFinder 2: New Methods for Selecting Partitioned Models of Evolution for Molecular and Morphological Phylogenetic Analyses." *Molecular Biology and Evolution* 34: 772–773. https://doi.org/10.1093/molbev/msw260.

Lenth, R. V. 2022. "emmeans: Estimated Marginal Means, aka Least-Squares Means R Package Version 1.7.3."

Letunic, I., and P. Bork. 2021. "Interactive Tree of Life (iTOL) v5: An Online Tool for Phylogenetic Tree Display and Annotation." *Nucleic Acids Research* 49: W293–W296.

Li, C., M. M. Williams, Y.-T. Loh, G. I. Lee, and G. A. Howe. 2002. "Resistance of Cultivated Tomato to Cell Content-Feeding Herbivores Is Regulated by the Octadecanoid-Signaling Pathway." *Plant Physiology* 130: 494–503. https://doi.org/10.1104/pp.005314.

Librado, P., and J. Rozas. 2009. "DnaSP v5." *Bioinformatics* 25: 1451–1452. https://doi.org/10.1093/bioinformatics/btp187.

Liu, J., R. Chafi, S. Legarrea, et al. 2020. "Spider Mites Cause More Damage to Tomato in the Dark When Induced Defenses Are Lower." *Journal of Chemical Ecology* 46: 631–641. https://doi.org/10.1007/s10886-020-01195-1.

Liu, J., S. Legarrea, J. M. Alba, et al. 2020. "Juvenile Spider Mites Induce Salicylate Defenses, but Not Jasmonate Defenses, Unlike Adults." *Frontiers in Plant Science* 11: 980. https://doi.org/10.3389/fpls.2020.00980.

Magalhães, S., J. Fayard, A. Janssen, D. Carbonell, and I. Olivieri. 2007. "Adaptation in a Spider Mite Population After Long-Term Evolution on a Single Host Plant." *Journal of Evolutionary Biology* 20, no. 5: 2016–2027.

Moran, P. J., and G. A. Thompson. 2001. "Molecular Responses to Aphid Feeding in Arabidopsis in Relation to Plant Defense Pathways." *Plant Physiology* 125, no. 2: 1074–1085.

Musser, R. O., S. M. Hum-Musser, H. Eichenseer, et al. 2002. "Herbivory: Caterpillar Saliva Beats Plant Defences." *Nature* 416: 599–600. https://doi.org/10.1038/416599a.

Nei, M. 1987. *Molecular Evolutionary Genetics*. Columbia University Press.

Paulo, J. T., D. P. Godinho, A. B. da Silva, C. Branquinho, and S. Magalhães. 2018. "Suppression of Plant Defenses by Herbivorous Mites Is Not Associated With Adaptation to Host Plants." *International Journal of Molecular Sciences* 19: 1783. https://doi.org/10.3390/ijms19061783.

Pilet-Nayel, M.-L., B. Moury, V. Caffier, et al. 2017. "Quantitative Resistance to Plant Pathogens in Pyramiding Strategies for Durable Crop Protection." *Frontiers in Plant Science* 8: 1838.
R Core Team. 2022. *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing.

Ronquist, F., M. Teslenko, P. van der Mark, et al. 2012. "MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space." *Systematic Biology* 61, no. 3: 539-542.

Rozas, J., J. C. Sánchez-DelBarrio, X. Meseguer, and R. Rozas. 2003. "DnaSP, DNA Polymorphism Analyses by the Coalescent and Other Methods." *Bioinformatics* 19: 2496-2497. https://doi.org/10.1093/bioinf-formatics/btg359.

Sarmento, R. A., F. Lemos, P. M. Bleeker, et al. 2011. "A Herbivore That Manipulates Plant Defence." *Ecology Letters* 14: 229-236. https://doi.org/10.1111/j.1461-0248.2010.01575.x.

Sarmento, R. A., F. Lemos, C. R. Dias, et al. 2011. "A Herbivorous Mite Down-Regulates Plant Defence and Produces Web to Exclude Competitors." *PLoS One* 6: e23757.

Sato, Y., J. M. Alba, and M. W. Sabelis. 2014. "Testing for Reproductive Interference in the Population Dynamics of Two Congeneric Species of Herbivorous Mites." *Heredity* 113: 495-502. https://doi.org/10.1038/hdy.2014.53.

Schimmel, B. C. J., J. M. Alba, N. Wybouw, et al. 2018. "Distinct Signatures of Host Defense Suppression by Plant-Feeding Mites." *International Journal of Molecular Sciences* 19: 3265. https://doi.org/10.3390/ijms19103265.

Schimmel, B. C. J., L. M. S. Ataíde, R. Chafi, et al. 2017. "Overcompensation of Herbivore Reproduction Through Hyper-Suppression of Plant Defenses in Response to Competition." *New Phytologist* 214: 1688-1701. https://doi.org/10.1111/nph.14543.

Stephens, M., N. M. J. Smith, and P. Donnelly. 2001. "A New Statistical Method for Haplotype Reconstruction From Population Data." *American Journal of Human Genetics* 68: 978-989. https://doi.org/10.1086/319501.

Sterck, L., K. Billiau, T. Abeel, P. Rouzé, and Y. Van de Peer. 2012. "ORCAE: Online Resource for Community Annotation of Eukaryotes." *Nature Methods* 9, no. 11: 1041.

Sukumaran, J., and M. T. Holder. 2010. "DendroPy: A Python Library for Phylogenetic Computing." *Bioinformatics* 26: 1569-1571. https://doi.org/10.1093/bioinformatics/btq228.

Tajima, F. 1983. "Evolutionary Relationship of DNA Sequences in Finite Populations." *Genetics* 105: 437-460. https://doi.org/10.1093/genetics/105.2.437.

Tamura, K., G. Stecher, and S. Kumar. 2021. "MEGA11: Molecular Evolutionary Genetics Analysis Version 11." *Molecular Biology and Evolution* 38: 3022-3027.

Teodoro-Paulo, J., J. M. Alba, S. Charlesworth, M. R. Kant, S. Magalhães, and A. B. Duncan. 2023. "Intraspecific Variation for Host Immune Activation by the Spider Mite *Tetranychus evansi*." *Royal Society Open Science* 10: 230525. https://doi.org/10.1098/rsos.230525.

Turrà, D., S. Vitale, R. Marra, S. L. Woo, and M. Lorito. 2020. "Heterologous Expression of PKPI and Pin1 Proteinase Inhibitors Enhances Plant Fitness and Broad-Spectrum Resistance to Biotic Threats." *Frontiers in Plant Science* 11: 461. https://doi.org/10.3389/fpls.2020.00461.

UniProt Consortium. 2019. "UniProt: A Worldwide Hub of Protein Knowledge." *Nucleic Acids Research* 47: D506-D515.

Van Leeuwen, T., and W. Dermauw. 2016. "The Molecular Evolution of Xenobiotic Metabolism and Resistance in Chelicerate Mites." *Annual Review of Entomology* 61: 475-498. https://doi.org/10.1146/annurev-ento-010715-023907.

Vega-Muñoz, I., D. Duran-Flores, Á. D. Fernández-Fernández, J. Heyman, A. Ritter, and S. Stael. 2020. "Breaking Bad News: Dynamic

Molecular Mechanisms of Wound Response in Plants." *Frontiers in Plant Science* 11, no. 610: 445.

Verwoerd, T. C., B. Dekker, and A. Hoekema. 1989. "A Small-Scale Procedure for the Rapid Isolation of Plant RNAs." *Nucleic Acids Research* 17: 2362.

Villacis-Perez, E., J. M. Alba, J. Cotte, Z. Van Loon, J. A. Breeuwer, and T. Van Leeuwen. 2022. "Interactions With Plant Defences Isolate Sympatric Populations of an Herbivorous Mite." *Frontiers in Ecology and Evolution* 346.

Villarroel, C. A., W. Jonckheere, J. M. Alba, et al. 2016. "Salivary Proteins of Spider Mites Suppress Defenses in *Nicotiana benthamiana* and Promote Mite Reproduction." *Plant Journal* 86: 119-131. https://doi.org/10.1111/tpj.13152.

Villesen, P. 2007. "FaBox: An Online Toolbox for Fasta Sequences." *Molecular Ecology Notes* 7: 965-968. https://doi.org/10.1111/j.1471-8286.2007.01821.x.

Vlot, A. C., D. A. Dempsey, and D. F. Klessig. 2009. "Salicylic Acid, a Multifaceted Hormone to Combat Disease." *Annual Review of Phytopathology* 47: 177-206. https://doi.org/10.1146/annurev.phyto.050908.135202.

Walling, L. L. 2000. "The Myriad Plant Responses to Herbivores." *Journal of Plant Growth Regulation* 19: 195-216. https://doi.org/10.1007/s003440000026.

Walsh, P. S., D. A. Metzger, and R. Higuchi. 1991. "Chelex 100 as a Medium for Simple Extraction of DNA for PCR-Based Typing From Forensic Material." *BioTechniques* 10: 506-513.

Wasternack, C., and B. Hause. 2013. "Jasmonates: Biosynthesis, Perception, Signal Transduction and Action in Plant Stress Response, Growth and Development. An Update to the 2007 Review in Annals of Botany." *Annals of Botany* 111: 1021-1058. https://doi.org/10.1093/aob/mct067.

Wybouw, N., V. V. Zhurov, V. Zhurov, et al. 2015. "Adaptation of a Polyphagous Herbivore to a Novel Host Plant Extensively Shapes the Transcriptome of Herbivore and Host." *Molecular Ecology* 24: 4647-4663. https://doi.org/10.1111/mec.13330.

Yang, X., D. C. Margolies, K. Y. Zhu, and L. L. Buschman. 2001. "Host Plant-Induced Changes in Detoxification Enzymes and Susceptibility to Pesticides in the Twospotted Spider Mite (Acari: Tetranychidae)." *Journal of Economic Entomology* 94: 381-387.

Zaidi, S. S.-A., M. S. Mukhtar, and S. Mansoor. 2018. "Genome Editing: Targeting Susceptibility Genes for Plant Disease Resistance." *Trends in Biotechnology* 36: 898-906.

Zavala, J. A., and I. T. Baldwin. 2004. "Fitness Benefits of Trypsin Proteinase Inhibitor Expression in *Nicotiana attenuata* Are Greater Than Their Costs When Plants Are Attacked." *BMC Ecology* 4: 1-15. https://doi.org/10.1186/1472-6785-4-11.

Zhang, D., F. Gao, I. Jakovlić, et al. 2020. "PhyloSuite: An Integrated and Scalable Desktop Platform for Streamlined Molecular Sequence Data Management and Evolutionary Phylogenetics Studies." *Molecular Ecology Resources* 20: 348-355. https://doi.org/10.1111/1755-0998.13096.

Zhurov, V. V., V. Zhurov, M. Navarro, et al. 2014. "Reciprocal Responses in the Interaction Between Arabidopsis and the Cell-Content-Feeding Chelicerate Herbivore Spider Mite." *Plant Physiology* 164: 384-399. https://doi.org/10.1104/pp.113.23155.23155.

## Supporting Information

Additional supporting information can be found online in the Supporting Information section.