DOI: 10.5061/dryad.rbnzs7hr8
title: Solar parks provide heterogeneous habitats for winter-active ground-dwelling predatory arthropods

description: Winter-active ground-dwelling spiders and ground beetles mainly inhabit
 non-crop habitats (e.g., grasslands and forests) with complex-structured
 vegetation and stable microclimates. In spring, they migrate from non-crop
 habitats into crop fields, contributing to pest control. Nowadays, there
 is an increasing number of solar parks in agricultural landscapes.
 However, the role of solar parks as habitats for winter-active
 ground-dwelling spiders and ground beetles, and thus their potential
 contribution to pest control, has not been studied yet. We investigated
 how different habitat types (i.e., forest, grassland, habitat between and
 under solar panels, and abandoned farmland) are associated with variation
 in microclimatic conditions (i.e., air temperature and humidity) and with
 the diversity of ground-dwelling spiders and ground beetles across 50
 sites in western Hungary. Using pitfall traps, we collected 957
 ground-dwelling spiders belonging to 69 species, and 327 ground beetles
 belonging to 40 species. We recorded microclimatic conditions using data
 loggers simultaneously with arthropod sampling. We showed that patterns in
 arthropod assemblages likely reflect differences in microclimatic
 conditions across habitat types. Solar parks hosted species of different
 habitats (e.g., forest, grassland, wetland) with a relatively strong
 preference for humidity. Solar parks also supported a high abundance of
 agrobiont ground-dwelling spiders (i.e., species dominant in
 agroecosystems). In contrast, grasslands and abandoned farmlands exhibited
 the most extreme microclimatic conditions, supporting mainly dry-habitat
 species. Our results demonstrate for the first time that solar parks can
 serve heterogeneous habitats for diverse assemblages of winter-active
 ground-dwelling spiders and ground beetles and may positively contribute
 to biocontrol and biodiversity conservation.
description: # Solar parks provide heterogeneous habitats for winter-active
 ground-dwelling predatory arthropods Dataset DOI:
 [10.5061/dryad.rbnzs7hr8](10.5061/dryad.rbnzs7hr8) ## Description of the
 data and file structure This dataset provides the data used to evaluate
 the association between different habitat types (i.e., forest, grassland,
 habitats between and under solar panels, and abandoned farmland) and
 variations in microclimatic conditions (i.e., air temperature and
 humidity), as well as the diversity of ground-dwelling spiders and ground
 beetles. Fifty sites in western Hungary were selected. The study sites
 were located around five settlements: Bicske, Csurgó, Nagyatád, Nyirád,
 and Óhíd. Near each settlement, we established two independent sampling
 sites in each of the following habitat types: forest, grassland, and
 abandoned farmland. In solar parks, two sampling sites were established
 within each park, placed 100 meters apart. Ground-dwelling predatory
 arthropods were collected using pitfall traps. Traps were open for two
 14-day periods: at the beginning of November (2023) and at the end of
 February (2024). The two pitfall traps from each sampling site were pooled
 across both sampling periods, resulting in 50 samples (5 settlements × 5
 habitats × 2 sites). Microclimatic conditions were recorded using data
 loggers (Optin TH4-32U) every 20 minutes for 11 days (from February 23 to
 March 4, during the second arthropod sampling period). Near each
 settlement, one data logger was placed in each habitat type, located at
 one of the two sampling sites for ground-dwelling predatory arthropods.
 ### Files and variables #### File: data.xlsx **Description:** The dataset
 contains five sheets: (1) microclimate, (2) spiders, (3) spiders_compo,
 (4) beetles, and (5) beetles_compo. Missing values ​​are marked as ‘NA’.
 *settlement*: Names of the settlements. *habitat*: Habitat types – forest,
 grassland, habitat between solar panels (‘between’), habitat under solar
 panels (‘under’), and abandoned farmland (‘abandoned’). *site_ID*: Site
 number within the given settlement, followed by the first (or first two)
 letters of the settlement name and the first letter of the habitat type.
 ##### **1) Microclimate** **Description:** Contains values of
 microclimatic variables to analyse the effect of habitat type on
 microclimatic conditions. Three sites where data loggers were lost were
 excluded from the dataset. *date*: Date of measurement. *day*: Order of
 the day of measurement. *datalogger_ID*: ID of the data logger. *humidity*
 (%), *min_temp* (°C), *max_temp* (°C), mean_temp
 (°C), *temp_variation* (°C): Values of environmental variables (temp =
 temperature) for each habitat type and settlement on each date. **2)
 Spiders** **Description:** Contains variables used to analyse the effect
 of habitat type on ground-dwelling spiders. *richness*: Number of spider
 species per site. *agrobiont*: Abundance of agrobiont spiders per site.
 *humidity*, *shading*: Community-weighted means (CWM) of humidity
 preference and shading tolerance per site. **3) Spiders_compo**
 **Description:** Contains a community matrix of pooled spider data from
 the two sampling periods, used to compare assemblage composition between
 habitat types. One site with zero individuals was excluded from the
 dataset. Each entry represents the Hellinger-transformed abundance of a
 given spider species at a site. **4) Beetles** **Description:** Contains
 variables used to analyse the effect of habitat type on ground beetles.
 *richness*: Number of ground beetle species recorded per site. *humidity*,
 *shading*: Community-weighted means (CWM) of humidity preference and
 shading tolerance per site. **5) Beetles_compo** **Description:** Contains
 a community matrix of pooled ground beetle data from the two sampling
 periods, used to compare assemblage composition between habitat types. Ten
 sites with zero individuals were excluded from the dataset. Each entry
 represents the Hellinger-transformed abundance of a given ground beetle
 species at a site. ## Code/software Analyses of the effect of habitat type
 on microclimatic conditions and ground-dwelling predatory arthropods were
 conducted using R (R Development Core Team, 2024), while comparisons of
 assemblage composition between habitat types were performed using CANOCO 5
 (ter Braak and Šmilauer, 2012). We tested the effect of habitat type on
 microclimatic conditions by generalized linear mixed models (GLMMs) with a
 Gaussian error structure using the ‘glmmTMB’ function in the ‘glmmTMB’
 package (Brooks et al., 2017). We added an autoregressive correlation
 structure to each model: ar1 (as.factor(day) + 0 | datalogger ID). To
 account for repeated observations within the same settlement and
 datalogger, settlement and datalogger ID (unique for each datalogger) were
 included as random effects in each model. We compared the species richness
 of both predatory groups and the abundance of agrobiont species of spiders
 between habitat types using generalized linear mixed-effects models
 (GLMMs) with a Poisson error structure (GLMM-p) using the ‘glmer’ function
 in the ‘lme4’ package (Bates et al., 2024). We used GLMMs with a negative
 binomial error structure (GLMMs-nb) if we detected overdispersion (Pekár
 and Brabec, 2009). The community-weighted means (CWMs) were calculated
 within the ‘functcomp’ function in the ‘FD’ package (Laliberté et al.,
 2024). We evaluated the effect of habitat type on trait composition (CWM)
 by linear mixed-effects models (LMMs) using the ‘lmer’ function in the
 ‘lme4’ package (Bates et al., 2024). We added settlement as a random
 effect to each model. Post hoc comparisons were made using the ‘glht’
 function in the ‘multcomp’ package (Hothorn, 2024). We compared the
 composition of ground-dwelling predatory arthropod assemblages between
 habitat types by canonical correspondence analysis (CCA). Only species
 with occurrence in more than three samples were selected for the analysis.
 The Hellinger transformation was applied to the species data before the
 calculation. The significance of the effects was tested using Monte Carlo
 permutation tests with 999 permutations within the blocks represented by
 the settlements. References: Bates, D., Maechler, M., Bolker, B. &
 Walker, S. (2024) lme4: Linear Mixed-Effects Models Using ‘Eigen’ and S4.
 R package version 1.1‑35.5. Brooks, M.E.J., Kristensen, K., van Benthem,
 K., Magnusson, A., Berg, C.W., Nielsen, A., Skaug, H.J., Mächler, M. &
 Bolker, B.M. (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](https://doi.org/10.32614/RJ-2017-066) Hothorn, T., Bretz, F. & Westfall, P. (2024) multcomp: Simultaneous Inference in General Parametric Models. R package version 1.4-26. Laliberté, E., Legendre, P. & Shipley, B. (2024) FD: Measuring Functional Diversity (FD) from Multiple Traits, and Other Tools for Functional Ecology. R package version 1.0-12.3. Pekár, S. & Brabec, M. (2009) Moderní analýza biologických dat. 1. vydání. Zobecněné lineární modely v prostředí R. Scientia, Praha. [in Czech]. R Development Core Team (2024) R: A Language and Environment for Statistical Computing. Version 4.4.0. R Foundation for Statistical Computing, Vienna [software]. Available at: [http://www.Rproject.org/](http://www.Rproject.org/) ter Braak, C.J.F. & Šmilauer, P. (2012) Canoco 5. Software for Multivariate Data Exploration, Testing, and Summarization. Netherlands [software].