DOI: 10.5061/dryad.hmgqnk9th
title: Trait records of marked Salvia nemorosa L. individuals censused annually between 2021-2023 in 13 localities of the Great Hungarian Plain

description: Predicting how changes in weather patterns and land use jointly impact
 populations is a pressing task in ecology. Microclimate may play a key
 role in species’ local persistence by modulating regional weather effects.
 We lack sufficient empirical evidence to understand the relative effects
 of landscape structure and habitat conditions on intraspecific trait
 variation. We used this spatially and temporally replicated
 dataset to test the relative effect of landscape structure (area and
 connectivity of remnant habitat fragments), microclimate (heat load), and
 fluctuation in weather conditions (study year) on intraspecific plant
 trait variation, and we investigated whether the local heat load modulated
 the weather effects on the studied traits.
description: The study was located in the Great Hungarian Plain in Eastern
 Hungary, Europe. We worked on 13 sites (11 kurgans and two reference flat
 grasslands) where <em>Salvia nemorosa</em> was present,
 spanning across 135 km (E - W) and 79 km (N - S). Kurgans were selected to
 represent different habitat area (basal area ranged between 528 - 4,321
 m<sup>2</sup>) and connectivity (Hanski connectivity index
 ranged between 0 - 338.4). <em>S. nemorosa</em> grew in
 remnant grassland patches dominated by <em>Festuca
 rupicola. </em> Data was collected at peak
 flowering of <em>S. nemorosa </em>in June-July, over three
 consecutive years between 2021-2023. Eight sites were censused each year
 throughout the length of the study, further four sites were censused only
 twice as new sites were added to the study between 2022-2023, while one
 site was only censused once in 2021 due to subsequent census difficulties.
 We established up to four permanent transects of varying lengths (4-10 m),
 depending on the area of the habitat patch, where the number of
 individuals was representative of the habitat. On kurgans, <em>S.
 nemorosa</em> individuals were often limited to only one side of the
 kurgan (typically on South-Southwestern slopes or on the top), but on
 three kurgans a larger number of individuals allowed setting up transects
 on two contrasting slopes. Along each transect, we laid down permanent
 contiguous plots of 0.5 x 0.5 m, within which we permanently marked all
 <em>S. nemorosa </em>individuals with a numbered linoleum tag,
 including new individuals each consecutive year. For each plant, we
 recorded in-situ three vegetative traits: height of the tallest stem,
 number of stems, length and width of two biggest leaves forming a leaf
 pair, and two reproductive traits: inflorescence length and number of
 primary side inflorescence pairs. To calculate mean leaf area, we
 approximated the measured leaf shapes to a triangle, and we multiplied the
 leaf length and width, which we averaged across the two measured leaves.
 In this study, we narrowed our analyses only to mature plants, to capture
 maximum attainable growth responses that can be related to the
 reproductive effort of same individuals. We defined mature individuals as
 plants that had a flowering probability (i.e., the likelihood that a plant
 develops inflorescence) higher than 50%.
description: # Trait records of marked Salvia nemorosa L. individuals censused annually
 between 2021-2023 in 13 localities of the Great Hungarian Plain
 [https://doi.org/10.5061/dryad.hmgqnk9th](https://doi.org/10.5061/dryad.hmgqnk9th) ## Description of the data and file structure METHODOLOGICAL INFORMATION Description of methods used for collection/generation of data: The study was located in the Great Hungarian Plain in Eastern Hungary, Europe. We worked on 13 sites (11 kurgans and two reference flat grasslands) where *Salvia nemorosa* was present, spanning across 135 km (E - W) and 79 km (N - S). Kurgans were selected to represent different habitat area (basal area ranged between 528 - 4,321 m2) and connectivity (Hanski connectivity index ranged between 0 - 338.4). S. nemorosa grew in remnant grassland patches dominated by Festuca rupicola.  We recorded site (GPS coordinates, date of visit, management, habitat type), and transect level (cardinal aspect and slope) information. Data collection followed the protocol of Plantpopnet, a spatially distributed model system for population ecology (Buckley et al., 2019). Data was collected at peak flowering of *S. nemorosa* in June-July, over three consecutive years between 2021-2023. Eight sites were censused each year throughout the length of the study, further four sites were censused only twice as new sites were added to the study between 2022-2023, while one site was only censused once in 2021 due to subsequent census difficulties. We established up to four permanent transects of varying lengths (4-10 m), depending on the area of the habitat patch, where the number of individuals was representative of the habitat. On kurgans, *S. nemorosa* individuals were often limited to only one side of the kurgan (typically on South-Southwestern slopes or on the top), but on three kurgans a larger number of individuals allowed setting up transects on two contrasting slopes. Along each transect, we laid down permanent contiguous plots of 0.5 x 0.5 m, within which we permanently marked all S. nemorosa individuals with a numbered linoleum tag, including new individuals each consecutive year. For each plant, we recorded in-situ three vegetative traits: height of the tallest stem, number of stems, length and width of two biggest leaves forming a leaf pair, and two reproductive traits: inflorescence length and number of primary side inflorescence pairs. In this study, we narrowed our analyses only to mature plants, to capture maximum attainable growth responses that can be related to the reproductive effort of same individuals. We defined mature individuals as plants that had a flowering probability (i.e., the likelihood that a plant develops inflorescence) higher than 50%. Methods for processing the data: To calculate mean leaf area, we approximated the measured leaf shapes to a triangle, and we multiplied the leaf length and width, which we averaged across the two measured leaves. To identify plants that had a flowering probability (i.e., the likelihood that a plant develops inflorescence) higher than 50%, we modelled the flowering state (yes or no) as a function of stem height, by fitting a Generalized Linear Mixed-effects Model (GLMM) with binomial error distribution in the lme4 package in R (Bates et al., 2015). Fixed effects were the tallest stem height in interaction with study year, while study site, transect, plot and plant were nested random effects. We tested the model assumptions using the DHARMa package (Hartig, 2022). We then used the generic function predict to obtain the flowering probability values for each individual, and individuals predicted a flowering probability lower than 50% were removed from further analyses. We used the connectivity index by Hanski et al., (2000) to quantify the level of habitat isolation within a 300 m buffer around the focal habitat patch (a lower value indicating higher isolation. We set α (a species-specific parameter related to the species’ dispersal ability) and β (a parameter describing the scaling of immigration) to 5 based on the assumption of a relatively low dispersal of 200 m for S. nemorosa. The area of the neighbouring grassland was calculated from digitised present day (2014-2016) habitat maps (source: Unified National Projection System, 1:10,000 topographic map of Hungary (Institute and Museum of Military History, Budapest), Satellite images (Google Maps) and field surveys, as in Deák et al., 2021). We calculated the habitat area as the basal area of the kurgan or the area of the grassland patch using the same method as described above (Deák et al., 2021). To ease calculations due to the disproportionately large area of one of the reference grasslands compared to the area of the kurgans, we unified the Hanski connectivity and habitat area values for the two reference grasslands to the value of the grassland with the smaller area (232,061 m2). To characterise the microclimate of the different transects we used the heat load index, a direct measure of incident solar radiation on a land surface that combines aspect (converted into degrees, slope, and latitude values (McCune & Keon, 2002), and which we calculated with a function by (Zelený & Lin, (2019) in R, using the first equation: Heat load= e^((-1.467+1.582*cos(L)*cos(S)-1.500*cos(A)*sin(S)*sin(L)-0.262*sin(L)*sin(S)+0.607*sin(A)*sin(S))), where L is the site latitude, A is the aspect where *S. nemorosa* populations where found, and S is the slope angle of the transect. References: Bates, D., Mächler, M., Bolker, B. M., & Walker, S. C. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1–48. [https://doi.org/10.18637/JSS.V067.I01](https://doi.org/10.18637/JSS.V067.I01) Hartig, F. (2022). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. Hanski, I., Alho, J., & Moilanen, A. (2000). Estimating the Parameters of Survival and Migration of Individuals in Metapopulations. Ecology, 81(1), 239 [https://doi.org/10.2307/177147](https://doi.org/10.2307/177147) Deák, B., Bede, Á., Rádai, Z., Tóthmérész, B., Török, P., Nagy D., D., Torma, A., Lőrinczi, G., Nagy, A., Mizser, S., Kelemen, A., & Valkó, O. (2021b). Different extinction debts among plants and arthropods after loss of grassland amount and connectivity. Biological Conservation, 264, 109372. [https://doi.org/10.1016/j.biocon.2021.109372](https://doi.org/10.1016/j.biocon.2021.109372) McCune, B., & Keon, D. (2002). Equations for potential annual direct incident radiation and heat load. Journal of Vegetation Science, 13(4), 603–606. [https://doi.org/10.1111/J.1654-1103.2002.TB02087.X](https://doi.org/10.1111/J.1654-1103.2002.TB02087.X) Zelený, D., & Lin, T.-Y. (2019). Heat load calculation: Analysis of community ecology data in R. [https://www.davidzeleny.net/anadat-r/doku.php/en:customized_functions:heatload](https://www.davidzeleny.net/anadat-r/doku.php/en:customized_functions:heatload) Instrument- or software-specific information needed to interpret the data: R Studio version: 2024.4.2.764 R Core Team (2024). *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing, Vienna, Austria. [https://www.R-project.org/](https://www.R-project.org/). Standards and calibration information, if appropriate: n/a Environmental/experimental conditions: n/a ### Files and variables #### File: Salvia\_2021\_2024\_final\_dataset(Dryad).csv **Description:** Trait records of marked *Salvia nemorosa* L. individuals censused annually between 2021-2023 in 13 localities of the Great Hungarian Plain ##### Variables * c_year: census year; * date_number: numbered survey dates in each year starting from the 1st of June; * site_id: identity of the site * microsite_id: identity of the microsite within a site * trans_id: identity of the transect within a site * plot_id: identity of the plot within a transect * plant_id: identity of the plant within a transect * stem_hei_noinf_allplants: height (mm) of the tallest stem of a plant, without inflorescence * num_stems: number of stems in a plant * mean_leafarea: mean leaf area of the fourth leaf pair counted from the stem node at soil level * inf_len: length (mm) of the main inflorescence of the tallest stem * inf_phe: phenological status of the plant (bud, flo: flowering, dev: seeds developing, mat: seeds matured, dis: seeds dispersed) * Prim_side_inf_pairs: number of primary side inflorescence pairs * heatload: heat load value calculated as described above * habitat_area: habitat area calculated as described above * Hanski: Hanski index calculated as described above * log_heatload: log10 of heat load * log_habitat_area: log10 of habitat area Missing data code: NA ## Code/software R Studio version: 2024.4.2.764 R Core Team (2024). \_R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. [https://www.R-project.org/](https://www.R-project.org/). Microsoft Excel versions R script