Direct and indirect trade-offs between resistance, growth, and reproduction in the Japanese stinging nettle Urtica thunbergiana
Data files
Mar 17, 2025 version files 200.15 KB
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Characteristics_of_each_nettle_R1.xlsx
195.55 KB
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README.md
4.59 KB
Abstract
Most studies of trade-offs between defence, growth, and reproduction have examined pairwise correlations between these processes, used ratio-based measures for defence allocation such as allelochemical concentration and trichome density, and estimated resource allocation to growth and reproduction in terms of biomass. However, for statistical and biological reasons, it may be preferable to analyse these processes holistically, to use absolute amounts of resistant traits and leaf mass/area, and to measure growth and reproduction in terms of nodes with or without flowers.
We aimed to identify how leaf stinging hairs as resistant structures and leaf area as a functional trait affected growth and reproduction in the Japanese stinging nettle Urtica thunbergiana.
We conducted a greenhouse experiment with nettles derived from a population that has been historically exposed to heavy browsing by Sika deer (Cervus nippon) in Nara Park, Japan. We analysed causal relationships between stinging hair number, leaf area, growth rate, growth performance, and reproductive output using structural equation modelling (SEM). In this analysis we adopted newly developed indices for a plant’s stinging hair number and leaf area, and measures of growth and reproductive traits in terms of nodes instead of biomass.
There was a significant covariation between stinging hair number and leaf area. Stinging hair number had direct negative effects on growth rate and performance, while leaf area had positive direct effects on growth rate, growth performance and reproductive output. The growth rate had a significant direct positive effect on reproductive output, resulting in a significant indirect negative effect of stinging hair number on reproductive output.
This indicates that there is not only a trade-off between resistance and growth, but also an indirect trade-off between resistance and reproduction through reduced growth rate and suggests that U. thunbergiana sacrifices growth rather than reproduction to increase resistance. Our study provides future work on trade-offs between defence, growth, and reproduction with a new methodological framework that can assess indirect as well as direct trade-offs, together with the effects of leaf area as a functional trait on these processes.
We collected nettle seedlings in Nara Park in early spring and raised them in a greenhouse near the park. For each plant that survived in early November, we counted the stinging hairs on the upper surface of the leaves along the main axis and measured the leaf area (cm^2). We also counted the total number of nodes and the number of inflorescence-producing nodes.
Description of the data and file structure
This file contains characteristic data for each nettle used for hierarchical linear model analysis of the relationship between leaf area and stinging hair number and structural equation model analysis of trade-offs between resistance, growth and reproduction. Leaf area and stinging hair number were corrected with reference to undamaged part when leaves were damaged by insects and viruses.
Sheet Leaves
“Leaf traits”
- Leaf code
- NA_ID: Plant ID
- Node #: Node number along the main axis from the base
- Leaf #: Leaf number of a pair of leaves on the node
- Leaf area (cm^2)
- Sting hair number: The number of stinging hairs on the upper surface of the leaf
- Density (/cm^2): The density of stinging hairs per cm^2
Sheet Node_production
“Tables for estimation of growth rate, consisting of the number of nodes and the number of nodes with inflorescences along the main axis on each census date (MM/DD in 2021)”
Sheet Number_of_nodes
“Tables for estimation of growth performance and reproductive output”
- The number of primary shoots
- The number of secondary and thirdly shoots along the primary shoots
- The total number of nodes along the all shoots (This was used as growth performance.)
- The number of nodes along the primary shoots
- The number of nodes along the secondary and thirdly shoots
- Total number of flower nodes: The total number of nodes with inflorescences (This was used as reproductive output.)
- Sheet Growth_rate: Tables for calculation of the growth rate for each plant (i.e., number of nodes at the 1st inflorescence emergence / days from 5th April to 1st inflorescence emergence day), consisting of the date of emergence of true leaves and inflorescences at each node along the main axis.
Sheet Data_for_HLM
“Table for conducting hierarchical linear modelling analysis”
- 21NA ID: Plant ID
- Node #: Node number along the main axis from the base
- Leaf #: Leaf number of a pair of leaves on the node
- Leaf area (cm^2)
- Stinging hair number: The number of stinging hairs on the upper surface of the leaf
- Plant mean LA (LA_m): Plant mean leaf area
- Centered at LA_m (LA(wc)): Leaf area centered at plant mean leaf area
- Centered at grand mean LA: Leaf area centered at grand mean leaf area
- Plant mean SHN: Plant mean number of stinging hairs
- Plant mean LA: Plant mean leaf area
- Plant mean LA_(gm): Plant mean leaf area centered at grand mean leaf area
Sheet Data_for_SEM
Table for conducting structural equation modelling analysis
- Plant ID
- Stinging hair number index*
- Leaf area index*
- Growth rate: see Sheet Growth_rate:
- Growth performance: see Sheet Number_of_nodes
- Reroductive output: Sheet Number_of_nodes
- *: We computed the indices as follows. To begin with, for each plant, we calculated the mean area (or mean number of stinging hairs) of the two leaves at each node along the main axis (when only one leaf was collected, its value was used). We then combined the data from all plants for each node and obtained the first and third quartiles of each sample. Next, for each plant, we assigned a score of +1 to a node if the leaf area at that node was greater than the third quartile, 0 if it was between the first and third quartiles, and −1 if it was less than the first quartile. Finally, for each plant, we calculated the value by dividing the total score by the number of nodes and used this as an index of a plant’s leaf area (or stinging hair number).
Authors
Yuzaki Inori
Department of Biological Sciences, Faculty of Science, Nara Women’s University, Nara, 630-8263 JAPAN
Minami Oishi
Department of Biological Sciences, Faculty of Science, Nara Women’s University, Nara, 630-8263 JAPAN
minami-needs-ranko@ezweb.ne.jp
Hiroaki Sato
Department of Biological Sciences, Faculty of Science, Nara Women’s University, Nara, 630-8263 JAPAN
In our data set, the sampling unit of leaf was nested within plant. We therefore constructed a two-level hierarchical linear model (HLM) to examine the relationship between leaf area and stinging hair number at the within-plant and between-plant levels:
Within-plant level
Yij = β0j + β1j Xij + rij
Between-plant level
β0j = γ00 + γ01 Gj + u0j
β1j = γ10 + γ11 Gj + u1j
where
Yij = stinging hair number for leaf i of plant j
Xij = leaf area for leaf i of plant j
Gj = mean leaf area of plant j
β0j = plant-specific intercept
β1j = plant-specific slope
γ00 = overall mean intercept (fixed effect)
γ10 = overall mean slope (fixed linear effect of leaf area)
γ01 = regression coefficient associated with mean leaf area of plant relative to plant-specific intercept (fixed linear effect of mean leaf area of plant)
γ11 = regression coefficient associated with mean leaf area of plant relative to plant-specific slope (fixed effect of the interaction between leaf area and mean leaf area of plants)
u0j = random effect of plant j on plant-specific intercept
u1j = random effect of plant j on plant-specific slope
rij = residual
In this model, Xij was centred at Gj, while Gj was centred at the grand mean value. The parameters γ10 and γ11 represent the effects of leaf area at the within- and between-plant levels, respectively. We then tested the statistical significance of the fixed and random effects. In this procedure, the parameters were estimated by maximum likelihood and robust standard errors were calculated. The parameters γ00, γ01, γ10, and γ11 and the variance components of u0j and u1j were tested using t-test and likelihood ratio test, respectively.
Structural equation modelling analysis (SEM)
We conducted SEM to examine the effects of leaf area and stinging hair number on growth rate, growth performance, and reproductive output as follows. Firstly, we built the full model under the hypotheses that (1) leaf area and stinging hair number covary; (2) both leaf area and stinging hair number affect the growth and reproductive components; (3) growth rate affects both growth performance and reproductive output; and (4) growth performance also affects reproductive output. Secondly, we constructed multiple models by leaving or removing insignificant (P > 0.05) paths from the initial model. Next, we computed the χ2 measure of goodness of fit (except for the full model due to lack of degrees of freedom) and the corrected Akaike information criterion for small sample sizes (AICC) for the full and each improved model. We then selected the model with an insignificant χ2 and the lowest AICC as the best model that fit the data better than any of the other models. Finally, we evaluated the relationships between the variables on the basis of the path coefficients of the best model.