Variation of Multiple Plant Defenses and Herbivory Reveal Different Patterns of Adaptation Across a Productivity Gradient

Variation of Multiple Plant Defenses and Herbivory Reveal Different Patterns of Adaptation Across a Productivity Gradient | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Variation of Multiple Plant Defenses and Herbivory Reveal Different Patterns of Adaptation Across a Productivity Gradient View ORCID Profile Jacob E. Herschberger , View ORCID Profile Eduardo S. Calixto , Valerie R. Campbell , View ORCID Profile John L. Maron , View ORCID Profile Philip G. Hahn doi: https://doi.org/10.1101/2025.10.31.684923 Jacob E. Herschberger 1 Entomology and Nematology Department, University of Florida , Gainesville, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jacob E. Herschberger For correspondence: j.herschberger{at}ufl.edu Eduardo S. Calixto 1 Entomology and Nematology Department, University of Florida , Gainesville, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eduardo S. Calixto Valerie R. Campbell 1 Entomology and Nematology Department, University of Florida , Gainesville, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site John L. Maron 2 Division of Biological Science, University of Montana , Missoula, MT, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John L. Maron Philip G. Hahn 1 Entomology and Nematology Department, University of Florida , Gainesville, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philip G. Hahn Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Plants with large geographic distributions experience varying biotic and abiotic conditions across their geographic range that can influence plant defense traits among plant populations. It is often the case that more productive regions support higher herbivore numbers. Yet whether this results in greater herbivore pressure and stronger plant defenses in these populations compared to populations inhabiting less productive regions is uncertain. Here we quantified defense traits and herbivory of Solanum carolinense L . across a large productivity gradient that spans its entire north-south range (29–44° N), using both field observations (33 populations; ∼14 plants per population) and a common garden experiment (15 populations; ∼4 plants per population). We examined the effects of productivity on 1) plant defense traits (specific leaf area, trichome density and glycoalkaloid concentration), 2) herbivory, and 3) the correlation among traits and herbivory in field and common garden plants. Trichome density and herbivory were higher for S. carolinense at the center of its range, while glycoalkaloid concentrations were negatively associated with productivity both in the field and in the common garden. In the field, plants with higher glycoalkaloid production experienced reduced herbivory, but there was no association with plant defense traits and herbivory in the common garden. Overall, these findings suggest that different types of defensive traits within a single species may follow different ecological and evolutionary trends and highlight the need for trait-specific considerations when applying plant defense hypotheses at the intraspecific level. Introduction Many plant species are distributed across large geographic ranges and therefore experience different abiotic and biotic conditions across their range ( Abdala-Roberts et al., 2016 ; López-Goldar and Agrawal, 2021 ). Plants adapt to varying abiotic and biotic stressors by altering various physiological or defense traits ( Bickford, 2016 ; Agrawal et al., 2022 ). These phenotypic changes across geographical ranges can be due to plasticity, genetic differentiation, or both ( López-Goldar and Agrawal, 2021 ; Agrawal et al., 2022 ). A substantial body of work has examined the contributions of plasticity and genetic differentiation to plant physiological and life history traits ( Endara and Coley, 2011 ; Massad et al., 2011 ). However, previous studies examining defense traits and herbivory pressure across intraspecific plant populations that span environmental gradients demonstrate mixed outcomes ( Woods et al., 2012 ; Hahn et al., 2019 ; Agrawal et al., 2022 ; Croy et al., 2022 ). Herbivory is typically expected to increase as environmental conditions become more productive or host plants become more abundant ( Maron et al., 2014 ; Louthan et al., 2015 ; Anstett et al., 2016 ). Empirical studies and meta-analyses have demonstrated that herbivory is positively associated with a variety of productivity gradients, including latitude ( Pennings and Silliman, 2005 ; Pennings et al., 2009 ; Salazar and Marquis, 2012 ; Zvereva and Kozlov, 2021 ), climate ( Hahn et al., 2019 , 2021 ) or elevation ( Galmán et al., 2018 , 2019 ; Kozlov et al., 2022 ; Zvereva et al., 2022 ). However, other studies do not support these general trends and either find the opposite pattern ( Anstett et al., 2014 ; Moreira et al., 2015 , 2018 ; Kooyers et al., 2017 ), or a non-linear pattern ( Fagan and Bishop, 2000 ; Woods et al., 2012 ; Agrawal et al., 2022 ). This non-linear trend in herbivory across a productivity gradient can be influenced by the density of the host plant species which can be more abundant at the range center ( Woods et al., 2012 ; Agrawal et al., 2022 ) or the edge ( Fagan and Bishop, 2000 ). In general, herbivore abundance and how it is affected by overall community productivity and host plant abundance likely determines underlying spatial patterns of herbivory ( Crutsinger et al., 2013 ; Anstett et al., 2014 , 2016 ). Although one might expect plant defenses to vary in concert with herbivore pressure across a species’ distribution, both theoretical predictions and empirical results are mixed ( Moles et al., 2011 ; Anstett et al., 2016 ; Hahn and Maron, 2016 ). A set of general hypotheses assert that increasing herbivore pressure, for example towards the equator ( Pennings et al., 2009 ), in more productive areas ( Hahn and Maron, 2016 ; Kooyers et al., 2017 ; Hahn et al., 2019 ), or in the center of a geographic range ( Anstett et al., 2016 ; Agrawal et al., 2022 ), should lead to greater plant defense in those areas. An opposing prediction is that intraspecific plant defenses should be greater in low-resource/high-stress environments, which has also been supported by numerous studies ( Robinson and Strauss, 2018 ; Croy et al., 2022 ; Calixto et al., 2025 ). Plant growth is greater in more productive regions; therefore, plants may be able to outgrow herbivory or replace plant tissue at higher rates compared to plants in slow growth environments. This suggests that, in more abiotically stressful environments, defenses which reduce herbivory might be more valuable compared to more productive environments ( Coley et al., 1985 ; Hahn and Maron, 2016 ). Thus, plant defense trends across a species’ range might differ depending on whether they are driven by herbivory levels or the resource availability for plant growth potential. In this study, we used a combination of field observations and a common garden experiment with Solanum carolinense L. to quantify multiple defense traits (specific leaf area, trichome density and glycoalkaloid concentration) and herbivory across a productivity gradient. We used the entire south-north (29-44 ° N) species range to disentangle plant defense-herbivore associations. Using a piecewise structural equation model (pSEM), we took a holistic approach that addressed the following questions: 1) Do herbivory and plant defenses have a linear or non-linear association with productivity? 2) Which defense traits are correlated with herbivory. And 3) Do herbivory and defense trait trends persist when plants are grown in a common garden? This study enabled us to better understand plant-herbivore interactions across intraspecific populations across a latitudinal gradient. Materials and Methods Study species and populations Solanum carolinense L. is an herbaceous perennial plant native to the eastern United States (29-44 ° N and 70-98 ° W; Fig. 1A ). It grows in disturbed areas such as lawns, roadsides, and crop fields ( Solomon, 1986 ; Kariyat et al., 2013 ). Flowering occurs mostly in June and July, and fruiting occurs in August and September ( Bassett and Munro, 1986 ; Kariyat et al., 2013 ). Solanum carolinense is a close relative of crop species such as S. lycopersicum L. (Tomato), S. melongena L. (Eggplant), and S. tuberosum L. (Potato). Plants in the genus Solanum produce multiple different glycoalkaloids that function as a defense against various leaf eating herbivores ( Chowański et al., 2016 ; Zhao et al., 2021 ; Hauri et al., 2024 ). The two dominant glycoalkaloid compounds produced by S. carolinense are α-solasonine and α-solamargine, which are effective in reducing leaf feeding, decreasing fecundity, and decreasing the heart rate through acetylcholinesterase inhibition of various insect herbivore species ( Wierenga and Hollingworth, 1992 ; Cipollini and Levey, 1997a , b; Güntner et al., 2000 ; Chowański et al., 2016 ). Solanum carolinense also has physical herbivore defenses including prickles and trichomes that are effective in protecting the plant against larger insect and non-insect herbivores ( Kariyat et al., 2013 , 2017 ; Nihranz et al., 2019 ). Lastly, this plant can tolerate some levels of herbivory by compensatory regrowth and asexual rhizomatous growth ( Wise and Mudrak, 2021 ). Common insect species that feed on this plant across its range are leaf beetle herbivores (Coleoptera: Chrysomelidae) such as Leptinotarsa juncta G. (False Colorado Potato Beetle), Epitrix fuscula C. (Eggplant Flea Beetle), and Gratiana pallidula B. (Eggplant Tortoise Beetle) ( Wise, 2007 & personal observations). Leaf herbivory is also less frequently incurred by herbivores such as Tildenia inconspicuella M. (Eggplant Leaf Miner), Gargaphia solani H. (Eggplant Lace Bug), Prodiplosis longifila G . (Citrus Gall Midge), and sometimes the generalist herbivore Manduca sexta L. (Tobacco Hornworm) ( Wise, 2007 ) as well as other generalist herbivores. Download figure Open in new tab Figure 1. A) A map of Solanum carolinense observed field plant populations (large points) and Inaturalist observations (small points). B) A principal component analysis where PC1 (C: i.e. productivity) was explained by annual precipitation (D: AP), mean annual temperature (E: MAT), precipitation of the warmest quarter (F: PWQ), and yearly standard deviation of temperature (G: Tsd). The percentages in (B) indicate the variation explained on each PC axis. We selected a south-north transect across the entire S. carolinense native range spanning approximately 15 ° in latitude, from 29-44 ° N. Mean annual temperatures and precipitation across this range vary from 8-21 ° C and from 908-1508 mm, respectively ( Fig. 1A ). During summers of 2022 and 2023 we sampled 33 populations across the geographic gradient. We sampled S. carolinense populations that contained at least six ramets more than two meters apart. Twenty-one populations were sampled in 2022, 11 populations were sampled in 2023, and one population was sampled in 2022 and 2023. Climate data extraction The worldclim_global_tile function from the geodata R (version 4.3.3) package was used to download monthly, 30 second resolution, historic (1970-2000) maximum temperature, minimum temperature, and precipitation ( Hijmans et al., 2023 ). We used the extract function in the R raster package to extract the prior three climate variables at each study site ( Hijmans, 2023 ). Then the mean annual temperature (MAT), annual precipitation (AP), standard deviation of yearly temperature (Tsd), and the precipitation of the warmest quarter (PWQ) was calculated using the biovars function in the dismo R package ( Hijmans et al., 2024 ). We chose MAT, AP, and PWQ since temperature and precipitation can both increase primary productivity across terrestrial ecosystems ( Del Grosso et al., 2008 ). Additionally, Tsd was found to be influential on the production of chemical defenses across a latitudinal gradient ( Anstett et al., 2018 ). To reduce the dimensionality of the four selected climate variables, we scaled and performed a principal component analysis (PCA) using the prcomp function in the base R stats package ( R Core Team, 2022 ). The first climate PC axis explained 86.7% and the second axis explained 10.4% of the variation ( Fig 1B ), where productivity associated variables such as AP ( Fig. 1D , r = 0.99), MAT ( Fig. 1E , r = 0.93), and PWQ ( Fig. 1F , r = 0.70) had a strong positive correlation, and Tsd ( Fig. 1G , r = −0.92) had a strong negative correlation with productivity PC1. Lastly, productivity PC1 had a strong correlation with latitude ( Fig. 1C ). We used the first PC axis (i.e. productivity PC) because it captures aspects of climate variation relevant to productivity and plant-herbivore interactions, despite the results being nearly identical if any of the individual variables or latitude was used as a proxy. Field within populations sampling Herbivory To determine herbivore damage across the geographic gradient, we sampled 10-30 individuals within each source population during June through early August of year 2022 or 2023. We randomly selected mature, budding or blooming plants further than two meters apart. We chose mature plants, so the observed herbivory is a good representation of cumulative herbivory that occurred throughout the growing season and is also when herbaceous plants switch from defending leaves to allocating resources to reproduction ( Boege and Marquis, 2005 ; Rusman et al., 2020 ). Herbivory was quantified by inspecting each leaf on the plant and visually estimating the total average herbivory of the whole plant ( Robinson et al., 2023 ). Since S. carolinense is relatively small (20-40 cm tall) and typically only produces 10-50 leaves per plant, we could easily visually estimate herbivory of the whole plant. Plant defense traits To determine how plant defense traits varied across latitudes, we measured trichomes and glycoalkaloids on the same 10-30 plants where herbivory was quantified. We also measured specific leaf area (SLA), which provides a leaf growth construction metric and can indicate plant palatability status ( Reich et al., 1999 ; Schädler et al., 2003 ; Agrawal and Fishbein, 2006 ). We collected the two top, fully developed leaves from each of the plants and oven-dried them at 50 ° C for 48 hours. Trichome density was estimated by counting the number of trichomes in a 0.24 cm 2 circle randomly placed between secondary leaf veins. We used a digital USB microscope (Aven 26700-209-PLR) at 5X magnification to photograph and count trichomes on the leaves. Specific leaf area (SLA; leaf area/mass) was measured by cutting a leaf piece from between the secondary leaf veins, measuring the area to the nearest 0.1 mm 2 , and then weighing the leaf to the nearest 0.1 mg. We measured the leaf area by first scanning the cut leaves with a Cannon scanner (K10486) and then used ImageJ to measure the leaf area. Lastly, total glycoalkaloids were measured using a direct hydrolysis-colorimetric protocol, modified slightly from previous studies ( Cipollini and Levey, 1997a ; Walls et al., 2005 ). Although, this method does not quantify specific glycoalkaloids, S. carolinense only produces two glycoalkaloid variants (α-solasonine and α-solamargine); both are effective in defending against herbivores ( Wierenga and Hollingworth, 1992 ; Güntner et al., 2000 ; Chowański et al., 2016 ), and this protocol has been commonly used in previous studies that quantify total glycoalkaloids in S. carolinense ( Cipollini and Levey, 1997b , a; Walls et al., 2005 ). We scaled the glycoalkaloid quantification protocol to use a microplate reader rather than a cuvette reader that had been used in previous protocols. First, we weighed and ground 20 ± 2 mg of leaf material to a fine powder with three 1.4 mm Fisherbrand ceramic beads in 2 ml microcentrifuge tubes. Then we added 0.5 ml of 0.5 M HCl and incubated the sample in a 100°C water bath for two hours. Next, we neutralized the solution with 0.5 ml of 0.5 M NaOH and added 0.5 ml acetic acid. After centrifuging the solution for five minutes at 12000 g, we transferred 0.9 ml of the supernatant to a new 2 ml tube and added 0.6 ml DI H 2 O. We then used 0.5 ml of the previous sample and added 0.5 ml acetate buffer (5.44 g sodium acetate, 100 ml DI H 2 O, and 2.4 ml acetic acid; PH 4.7), 0.1 ml methyl orange, and 0.5 ml methylene chloride. After mixing this solution for three minutes and allowing to settle for three minutes, we pipetted 200 ul of the infranatant into a u-shaped, glass-bottomed 96 well microplate and measured the absorbance at 420 nm using a microplate reader (Multiskan™ SkyHigh, Thermo-Scientific, United States). Common garden study design To quantify herbivore damage and plant defense in the absence of spatial environmental variability, we grew S. carolinense in a common garden in an old-field site on the University of Florida campus (Gainesville, Florida USA; lat. 29.626799, long. −82.356232) from April to August 2023. We used roots collected from ∼6 plants 2 m apart within each population during 2022 (n = 15 populations and 67 total plants). We cut the roots to 0.5-1 g each before planting the cuttings in 3.8 L pots that contained a 1:1 mixture of topsoil and potting mix. Plants were started in the greenhouse on April 19, 2023, in a temperature range of 21-32° C, a photoperiod of approximately 13 L:11 D, and 60% relative humidity. After 4 weeks of growth, the plants were transferred to the common garden and placed on wooden 1.2×1.2 m pallets. We then observed herbivory every 2-3 weeks during the growing season. We used herbivory data from early June to late July to be consistent with field herbivory data in terms of the plant phenological stage ( Fig. S1 ). This timeline provides good representation of the accumulated herbivory, and the correlation between defense traits and herbivory since herbaceous plants invest less in defenses close to fruit development ( Boege and Marquis, 2005 ; Rusman et al., 2020 ). Additionally, we quantified SLA, trichomes, and glycoalkaloids in the same manner as in the field surveys. The defense traits were measured at the end of the growing season on August 17, 2023, to reduce the effects of harvesting leaves on herbivory and trait expression. Data and statistical analysis We used a piecewise structural equation model (pSEM) to test herbivory-defense relationships across the productivity gradient, which allowed us to simultaneously evaluate all relevant relationships between herbivory, plant traits, and how they vary across the productivity gradient. We used the psem function from the piecewiseSEM R package, which provides the option to include random effects and various generalized linear models ( Lefcheck, 2016 ). However, we only used linear mixed models to construct our pSEM, since the psem function does not allow standardized coefficients with beta distributions. We log-transformed trichomes, SLA, and Glycoalkaloids, and logit transformed herbivory to obtain normal distributions. Additionally, after transformations all the predictors and responses were centered and scaled (mean = 0, SD = 1) to increase model stability ( Schielzeth, 2010 ). Lastly, we removed extreme data points from the field (three) and common garden experiment (two), where leaf SLA values were more than three standard deviations from the mean. The pSEM structure used was as follows: the first sub-model included herbivory as the response and productivity PC1 as the predictor. Plant traits were also included as predictors to infer the relationship between herbivory and plant defense traits. To test whether plant defense traits are related to the productivity PC1, additional sub-models were included where each defense trait was a response variable and productivity PC1 as the predictor to test for clinal variation in each trait. The pSEM included productivity both as a quadratic term and a linear term to test whether there is a non-linear trend of defenses and herbivory across the gradient. We added a random effect by population in the common garden to account for individual variation within a population. In the field, the random effects were modeled as population:year to account for measures taken within each survey. We used the psem and summary functions from the piecewiseSEM package, respectively, to build the model and extract the statistics of the model ( Lefcheck, 2016 ). We used the Akaike Information Criterion (AIC) of the field models and the corrected AIC (AIC c ) of two garden models as a model selection criterion. The AIC c corrects for low sample size when the model parameters (K) are greater than the sample size (field n = 519, garden n = 67) divided by 40 ( Cardon et al., 2011 ; Lefcheck, 2016 ). For the final model, we also calculated the variance explained by the fixed effects (R 2 m), the variance explained by the random effects (i.e., intraclass correlation or ICC), and the variance explained by the full model (R 2 c) using the performance package ( Lüdecke et al., 2021 ). Results The field SEM demonstrated a hump-shaped quadratic relationship between trichomes and the productivity PC, where trichome density was higher at the center of the plant range ( Figs. 2A & 3A ; β x = −0.124, p = 0.27; β x 2 = −0.288, p = 0.02; R 2 m = 0.11; R 2 c = 0.43; ICC = 0.32). Glycoalkaloid concentrations were higher in low productive regions and had no quadratic relationship with productivity ( Figs. 2A & 3B ; β x = −0.325, p = 0.01; β x 2 = 0.145, p = 0.31; R 2 m = 0.10; R 2 c = 0.71; ICC = 0.61). Additionally, herbivory had a hump-shaped relationship with the productivity PC ( Figs. 2A & 3C ; β x = 0.312, p = 0.005; β x 2 = −0.407, p = 0.001; R 2 m = 0.21; R 2 c = 0.71; ICC = 0.35). There was no linear or quadratic trend between SLA and the productivity PC ( Fig. 2A ; β x = 0.046, p = 0.75; β x 2 = 0.070, p = 0.66; R 2 m = 0.01; R 2 c = 0.68; ICC = 0.67). The SEM demonstrated that herbivory was negatively correlated with glycoalkaloids ( Figs. 2A & 3D ; β x = −0.165, p = 0.002) but did not correlate with SLA (β x = −0.056, p = 0.27), or trichomes (β x = −0.023, p = 0.54). Download figure Open in new tab Figure 2. The pSEMs of A) field and B) garden interactions. Solid lines indicate a (marginally) significant (p < 0.1) relationship between the predictor and the response, and the dashed lines indicate a non-significant relationship. Black lines indicate a positive relationship, and red lines indicate a negative relationship. Standardized coefficient values are shown for (marginally) significant (p < 0.1) paths, and the widths of the lines indicate the strength of the at least marginally significant relationships. For plants grown in the common garden, we found that there were similar trends between plant defense traits and the productivity PC. Trichomes ( Figs. 2B & 4B ; β x = 0.238, p = 0.26; β x 2 = −0.418, p = 0.05; R 2 m = 0.15; R 2 c = 0.53; ICC = 0.38) had a hump-shaped quadratic relationship and glycoalkaloids had a minor negative relationship with productivity PC ( Figs. 2B & 4A; β x = −0.334, p = 0.07; β x 2 = −0.267, p = 0.15; R 2 m = 0.27; R 2 c = 0.56; ICC = 0.29), whereas there was no relationship between SLA and the productivity PC ( Fig. 2B ; β x = −0.003, p = 0.99; β x 2 = 0.039, p = 0.83; R 2 m = 0; R 2 c = 0.20; ICC = 0.20). Additionally, in contrast to patterns in the field, there was no relationship between herbivory and the productivity PC in the common garden ( Fig. 2B ; β x = −0.233, p = 0.11; β x 2 = 0.156, p = 0.28; R 2 m = 0.06; R 2 c = 0; ICC = 0.06). Lastly, there was no relationship between SLA ( Figs. 2B & 4C ; β x = 0.134, p = 0.26), glycoalkaloids (β x = −0.157, p = 0.28), or trichomes (β x = 0.129, p = 0.35) and herbivory. Discussion Empirical studies testing the association of a productivity gradient on plant defense-herbivore interactions are mixed when examined within species ( Moles et al., 2011 ; Woods et al., 2012 ; Moreira et al., 2018 ). However, previous studies typically only measure one defensive trait across only a portion of a species’ full distribution. We therefore quantified the association between productivity, herbivory, and multiple defense traits across populations that span the full south-north distribution of Solanum carolinese in both the field and a common garden. We found that defensive traits follow different patterns across a productivity gradient. Glycoalkaloid concentration had a negative association with the productivity gradient and trichomes density was the highest at the center of the gradient in both the field and common garden. Moreover, while higher glycoalkaloid concentrations correlated with reduced herbivory in the field, this trend did not persist in the common garden, maybe due to different herbivory types and intensity. Importantly, the results reveal that defense traits follow different patterns, demonstrating that some plant defenses may be influenced by herbivore pressure while other defense traits may be more influenced by productivity, which might be an indicator of overall resource availability. Trichomes were produced at higher densities in the middle of the productivity gradient, which corresponds to the center of the S. carolinense species range, with significantly lower densities toward both northern and southern range edges ( Figs. 2A, 2B , 3A , & 4A ). These results support the range center hypothesis where plant defense production coincides with higher specialist herbivore abundance at the center of a plant species range ( Anstett et al., 2016 ). Specialist herbivores are typically most abundant and diverse at the center of the host plant range due to better herbivore habitat connectivity and larger plant population sizes ( Crutsinger et al., 2013 ; Anstett et al., 2016 ). The main herbivores of S. carolinense are specialist chrysomelid beetle species, Leptinotarsa juncta, Epitrix fuscula , and Gratiana pallidula , which are more commonly found at center latitude populations and might impose selective pressure on S. carolinense trichomes (personal observation). The persistent hump-shaped relationship between trichome density and productivity in both field observations and in the common garden experiment indicates a genetic basis for trichome production (correlation of population means: r = 0.60, p = 0.02). This likely indicates that plants in our study are locally adapted to invest in physical defenses in response to spatial variation in herbivore abundance and diversity ( López-Goldar and Agrawal, 2021 ; Agrawal et al., 2022 ). In contrast to the pattern observed in trichomes, glycoalkaloid concentrations followed a negative linear relationship across the productivity gradient where less glycoalkaloids were produced in more productive regions. This trend, although marginally significant in the garden, occurred both in the field observations and the common garden plants originating from across the productivity gradient ( Figs. 2A, 2B , 3B , & 4B ). This pattern aligns with predictions of the resource availability hypothesis, which predicts greater investment towards costly defenses under resource-limited conditions ( Coley et al., 1985 ; Endara and Coley, 2011 ). Despite there being a weak correlation of glycoalkaloid production between field and common garden populations, similar trends seen in the garden and field suggests a weak genetic basis for glycoalkaloid production (correlation of population means: r = 0.25, p = 0.39). This might indicate a bottom-up, environmentally driven, selective pressure on glycoalkaloid production across plant populations rather than driven by herbivore pressure. Northern plant populations in our study system experience shorter growing seasons, and lower mean annual temperatures (8-21°C), conditions that could favor investment toward chemical defenses ( Endara and Coley, 2011 ). Typically, these patterns of greater defenses in resource poor environments are found across species but have also been documented across populations especially for chemicals that are costly to produce (e.g., terpenoids; Hahn et al., 2021 ; Pratt & Mooney, 2013 ). The slight genetic basis for glycoalkaloid variation is particularly significant given that these compounds require substantial metabolic investment but provide broad-spectrum protection against herbivores through detrimental nervous system overstimulation ( Wierenga and Hollingworth, 1992 ; Chowański et al., 2016 ). Similar to trichome density, herbivory damage was the highest at the center of the plant range in field plants ( Figs. 2A & 3C ) but did not follow a significant pattern in the common garden experiment. This contrast might suggest that local herbivore communities drive some spatial variation in trichomes, consistent with the range edge hypothesis prediction that specialist herbivores are most abundant at the center of the host plant ranges ( Woods et al., 2012 ; Anstett et al., 2016 ). Additionally, trichomes have been shown to defend against herbivores in S. carolinense ( Kariyat et al., 2017 ) and might have played a functional role in defending against herbivory at the population level in our study. The common garden was located at the southern edge of our study gradient (Gainesville, Florida, 29.6°N), where the local herbivore community differed from the herbivore communities in the field. The three Chrysomelidae species, Leptinotarsa juncta, Epitrix fuscula , and Gratiana pallidula , often found across the S. carolinense species range, were not observed in the common garden experiment (personal observation) and the most common herbivore was a generalist ( Spodoptera eridania , Lepidoptera: Noctuidae). The less robust herbivory pattern in the common garden suggests that environmental context is critical for understanding plant-herbivore interactions. Additionally, although the patterns for trichomes and herbivory were similarly hump-shaped, there was no direct correlation between herbivory and trichomes. What this finding suggests is that trichomes are adapting to long-term, consistently greater herbivore pressure in the center of the range ( Agrawal et al., 2022 ; Anstett et al., 2016 ). However, the effect was apparently not strong enough to detect a direct relationship between trichome density and herbivory in our field surveys. In contrast to trichomes, herbivory was negatively correlated with glycoalkaloid concentrations in the field, but no pattern was found in the common garden ( Fig. 3D ). This might suggest that there is a defensive function of glycoalkaloids against specialist herbivores seen in the field, but not the generalist herbivores in the common garden. Additionally, there was more herbivory pressure in the common garden (12.1%) versus the field (9.2%) (χ 2 = 27.71, df = 1, p < 0.001), which might have contributed to a different association between glycoalkaloids and herbivory in the field versus the common garden. This shift in plant herbivore-defense relationships across different traits, where trichomes appear to be driven by top-down forces whereas glycoalkaloids appear to be driven by bottom-up forces, underscores the complexity in determining defense effectiveness and evolution. Download figure Open in new tab Figure 3. The field, significant relationships of trichomes (A) glycoalkaloids (B) and herbivory (C) in relation to the productivity PC. Additionally, D) demonstrates herbivory in relation to glycoalkaloids. Transparent black data points indicate individual plants, and red data points indicate population averages. The lines indicate the generalized quadratic/linear trend of the relationship between the predictors and responses. Download figure Open in new tab Figure 4. Garden data plots of A) trichomes, B) glycoalkaloids, and C) herbivory in relation to the plant productivity PC origin. Transparent black data points indicate individual plants, and red data points indicate population averages. The lines indicate the marginally significant (p < 0.1) generalized quadratic/linear trend of the relationship between the predictors and responses. Here we demonstrate that plant defenses adapt differently across a productivity gradient, where trichome production appears to be influenced by herbivore pressure and glycoalkaloid production might be driven by climate productivity. The persistence of trichome and glycoalkaloid production trends across a productivity gradient in the field and plants derived from across a productivity gradient, indicates evolutionary adaptation to varying biotic and abiotic conditions. Overall, these findings suggest that different defensive traits within a single species likely follow different ecological and evolutionary trends, which could point to why plant defenses trends across a plant species range remain mixed. Author contributions J.E.H.: conceptualization; data curation; formal analysis; investigation; methodology; project administration; visualization; writing original draft, review and editing. E.S.C.: conceptualization; methodology; review and editing. V.R.C: methodology; review and editing. J.L.M.: conceptualization; funding acquisition; methodology; reviewing and editing. P.G.H.: conceptualization; data curation; formal analysis; funding acquisition; resources; investigation; methodology; supervision; review and editing. Data availability Data and code are currently archived on Github: https://github.com/Plant-herbivory-interaction-lab/Herbivory-and-plant-defense-patterns-across-environmental-gradients.git , and while also be available on Dryad upon manuscript acceptance. Conflict of interest The authors declare they have no conflict of interest. Acknowledgments Thanks to Rebecca Molina, Emma Hair and Lucia Navia who assisted with data collection. Thanks to Monica Paniagua Montoya and Alvin Roosevelt Diamond for providing us with Solanum carolinense population location prospects. This study was funded by the NSF grant # DEB-1901552 to JLM and PGH and USDA McIntire-Stennis FLA-ENY-006018. Appendix Download figure Open in new tab Figure S1. Graphs of A) leaves, B) proportion of plants flowering, and C) herbivory over time in the common garden. Data points indicate population averages at a given time point and the lines indicate trends between date and the responses. Colors indicate a specific source population of a plant. We used average herbivory measured from early June to late July for our final analysis. Download figure Open in new tab Figure S2. Graphs of herbivory in relation to defense traits at various time segments of the common garden experiment. Each individual data point indicates the plant average during a specific time interval and lines indicate trends at each time segment. Funder Information Declared NSF - Division of Environmental Biology , DEB-1901552 USDA - National Institute of Food and Agriculture , FLA-ENY-006018 Footnotes https://github.com/Plant-herbivory-interaction-lab/Herbivory-and-plant-defense-patterns-across-environmental-gradients Bibliography ↵ Abdala-Roberts , L. , X. Moreira , S. Rasmann , V. Parra-Tabla & K.A. 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Share Variation of Multiple Plant Defenses and Herbivory Reveal Different Patterns of Adaptation Across a Productivity Gradient Jacob E. Herschberger , Eduardo S. Calixto , Valerie R. Campbell , John L. Maron , Philip G. Hahn bioRxiv 2025.10.31.684923; doi: https://doi.org/10.1101/2025.10.31.684923 Share This Article: Copy Citation Tools Variation of Multiple Plant Defenses and Herbivory Reveal Different Patterns of Adaptation Across a Productivity Gradient Jacob E. Herschberger , Eduardo S. Calixto , Valerie R. Campbell , John L. Maron , Philip G. 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