DOI: 10.5061/dryad.w6m905r0j
title: Data from: Silicon mediates geographic variation of herbivory-related traits in a widespread plant invader

description: Silicon, the Earth’s second most abundant element, affects plant defenses
 across large geographic scales, which is an interesting yet mostly
 unexplored issue for non-native species. We investigated silicon-mediated
 variation in defensive/nutritional traits and palatability to generalist
 herbivores in 16 geographic populations of the invasive grass Spartina
 alterniflora collected from native and introduced ranges. We found that
 silicon supplementation generally increased leaf silicon content, which
 then enhanced plant physical/chemical defenses and reduced nutrition,
 ultimately impeding the growth of generalists. Yet, the pathways of
 silicon accumulation affecting generalists differed between plant
 provenances: enhancing quantitative and qualitative chemical defenses in
 native populations, but improving quantitative chemical and structural
 defenses in introduced ones. This suggests a silicon-mediated shift in
 Spartina’s defense strategy following introduction. Our findings provide
 insights into how non-native plants utilize silicon to enhance defenses,
 emphasizing the importance of metalloid defenses in invasion success.
description: # Data from: Silicon mediates geographic variation of herbivory-related
 traits in a widespread plant invader
 [https://doi.org/10.5061/dryad.w6m905r0j](https://doi.org/10.5061/dryad.w6m905r0j) ## Description of the data and file structure we examined 16 native and introduced populations of Spartina to investigate the geographic variation in its palatability to generalist herbivores and the associated traits in response to silicon supplementation. ### Files and variables #### File: Raw_data.xlsx **Description:** Raw_data.xlsx contains only one sheet: Plant traits & herbivore growth ##### Variables * Column 1: Si treatment — Si+/Si− * Column 2: Range — The original range of plant populations: native range, high-latitude introduced range and low-latitude introduced range * Column 3: Population — The abbreviation of plant populations: * MC: Morehead City, North Carolina * SI: Sapelo Island, Georgia * TB: Tampa Bay, Florida * TS: Tangshan, Hebei * DY: Dongying, Shandong * LYG: Lianyungang, Jiangsu * YC: Yancheng, Jiangsu * SH: Chongming, Shanghai * CX: Cixi, Zhejiang * TZ: Taizhou, Zhejiang * WZ: Wenzhou, Zhejiang * FZ: Fuzhou, Fujian * QZ: Quanzhou, Fujian * ZZ: Zhangzhou, Fujian * BH: Beihai, Guangxi * LZ: Leizhou, Guangdong * Column 4: Family — The propagule source * Column 5: Sqrt_Si (mg/g) — The square-root transformed leaf silicon content * Column 6: Ln_SLA (cm2/g) — The natural-log transformed specific leaf area * Column 7: Toughness (N) — The leaf toughness * Column 8: Sqrt_JA (mg/g) — The square-root transformed jasmonic acid content * Column 9: Tannins (mg/g) — The leaf tannin content * Column 10: Flavonoids (mg/g) — The leaf flavonoid content * Column 11: Carbon (mg/g) — The carbon content * Column 12: C: N ratio — The carbon-to-nitrogen ratio * Column 13: Sqrt_myth_Growth (mg) — The square-root transformed weight gain in fresh biomass of Mythimna separata * Column 14: Loc_growth (mg) — The weight gain in fresh biomass of Locusta migratoria ## Code/software All analyses were performed with R version 4.1.2 software. We used the lme4 package for linear mixed models (LMMs) to analyze differences in dependent variables as affected by silicon treatments and population ranges. To understand the relationships among silicon treatments, leaf traits, and generalist performance in each plant population range, we employed the lavaan package to construct three structural equation models (SEMs).