https://doi.org/10.5061/dryad.xksn02vr1

Description of the data and file structure

The dataset was collected in 1 hectare forest plots in the Black Forest to understand the direct and insect-prey mediated effects of  forest management and structure on bats.

It includes forest structural and compositional variables, and index describing the forest management intensity, understory vegetation variables, insect abundance and richness data, and activity data for three bat guilds (SRE,MRE,LRE bats).

Files and variables

File: Hendel_etal_2024_Data_DRYAD.xlsx

Description: 

The dataset was collected in 1 hectare forest plots in the Black Forest to understand the direct and insect-prey mediated effects of  forest management and structure on bats.

It includes forest structural and compositional variables, and index describing the forest management intensity, understory vegetation variables, insect abundance and richness data, and activity data for three bat guilds (SRE,MRE,LRE bats).

Variables

• plotID: Unique id for each forest site
• DOY: Day of year in 2020 that indicates the start of the data sampling period for the moths and bats.
• DOY_carabidae: Day of year in 2020 that indicates the start of the data sampling period for the ground beetles.
• temp_min: The average minimum night (20:00 to 07:00) temperature in degree Celsius during the sampling period for the moths and bats.
• elevation: Elevation in m at the forest site above sea level.
• ForMI: Forest management intensity index quantifies the intensity of past management operations in each plot (Kahl & Bauhus, 2014). The index integrates 1) proportions of harvested tree volume, 2) proportions of non-native tree species , and 3) proportions of dead wood showing signs of saw cuts. ForMI values increase with management intensity (Table 1). Data for the first two ForMI components originated from a full inventory in 2016/2017 (Storch et al., 2020), that included all trees with a diameter at breast height (1.3m, DBH) above 7 cm. Deadwood data were collected in 2020 (Asbeck & Frey, 2021).
• SR_trees: Tree species richness of trees above 7cm DBH (diameter at breast height, 1.3 m) in each forest site.data was collected as part of an forest inventory in 2016/2017 (Storch et al., 2020).
• cf_share: Share of coniferous trees in each forest site. It was was calculated as the proportion of the total basal area occupied by coniferous trees.
• ndsm_sd: The variable  represents the  forest height heterogeneity’. It represents the standard deviation (SD) in canopy heights, which were obtained from UAV imagery in 2019/2020 by using a structure-from-motion workflow (Frey et al., 2018).
• Opn_mean: The openness was measured using Solariscope SOL300 (Ing.-Büro Behling, Wedemark) at the top of the understory vegetation and describes the openness of the mid-and canopy-forest layers. In each plot, we took measurements between May - September 2020 at 18 systematically arranged locations.
• DBHMean: The average tree diameter at breast height (1.3 m) in mm per forest site. Data were collected during the inventory (Storch et al., 2020).
• ndead: The number of standing dead tree per plot. Data were collected during the inventory (Storch et al., 2020).
• TreMs_bat_A:Availability of Tree-related microhabitats that are potentially suitable as bat roots. Micorhabitats were inventoried in 2019, 2020 from the ground using binoculars. Surveys of the 15 largest living trees and, where present, up to 15 dead trees were done in the winter, when crown visibility was enhanced. As bats use particular microhabitat types for roosting, we used the abundance of cavities, branch-holes, exposed heartwood, cracks and scars, and bark shelters or pockets.
• understory_cover: Understory vegetation cover included all vascular plants in the herb layer (smaller than 1.50 m)  and surveys were conducted between May - September 2020. Understory cover were determined at 18 subplots of 1m2.
• SR_understory: Understory vegetation richness included all vascular plants in the herb layer (smaller than 1.50 m)  and surveys were conducted between May - September 2020. Understory species richness was summed for the 18 subplots of 1m2.
• mothcount: Moth abundance was determined using one ultra-violet (UV) light-trap per plot. The latter were flight interception traps (Knuff et al. 2019) equipped with UV fluorescence actinic tubes (15 W, Bioform, Article No.: A32b) in the middle. Light-traps were installed at 1.4 m height at the plot centre and were active once per plot for six hours after sunset between May - August 2020. Light-trapping was synchronised with the bat survey and was performed during the second of three consecutive sampling nights. Insects were caught in 50 % propylene glycol. Moth samples were counted in the lab.
• moth_richness: Species richness of the moths determined using metabarcoding. We use bulk sample metabarcoding and the retrieved barcodes were blasted against the Barcode of Life databank (BOLD, Ratnasingham & Hebert, 2007). Assignments with a sequence similarity of 97% or higher were considered reliable identifications. The number of reads was transformed into presence-absence records per species and plot. Species with less than 10 reads across all plots were excluded from the dataset and species with less than two reads per plot were treated as absence record.
• carabidae: Average abundance of ground beetle individuals per pitfall trap in each plot. Pitfall traps were installed separately, between April - May 2020, following the elevational gradient and were retrieved after approximately 36 days (Pereira et al., (2024)). One pitfall trap was set at the plot centre and two at the opposing plot corners. The traps included a rain cover and were filled with 250 mL of 50 % propylene glycol.
• carabidae_S: Average species richness of ground beetles per pitfall trap in each plot.
• SRE: Activity of short-range echolocating bat species. Bat activity was expressed as the average number of 1-minute intervals per night containing echolocation calls of that bat guild per night (Müller et al., 2012). As light-traps modify bat behaviour (Froidevaux et al., 2018), we used bat activity data from the first and third night, when UV-light-traps were inactive. SRE bats included Myotis spp.,Barbastella barbastellus and Plecotus spp.
• MRE: Activity of mid-range echolocating bat species. Bat activity was expressed as the average number of 1-minute intervals per night containing echolocation calls of that bat guild per night (Müller et al., 2012). As light-traps modify bat behaviour (Froidevaux et al., 2018), we used bat activity data from the first and third night, when UV-light-traps were inactive. MRE bats included Pipistrellus spp except P.pipistrellus and Hypsugo savii.
• Ppip: Activity of Pipistrellus pipistrellus. Bat activity was expressed as the average number of 1-minute intervals per night containing echolocation calls (Müller et al., 2012). As light-traps modify bat behaviour (Froidevaux et al., 2018), we used bat activity data from the first and third night, when UV-light-traps were inactive.
• LRE: Activity of long-range echolocating bat species. Bat activity was expressed as the average number of 1-minute intervals containing echolocation calls of that bat guild per night (Müller et al., 2012). As light-traps modify bat behaviour (Froidevaux et al., 2018), we used bat activity data from the first and third night, when UV-light-traps were inactive. LRE bats included Eptesicus spp., Nyctalus spp. and Vespertilio murinus.

References:

Asbeck, T., Pyttel, P., Frey, J., & Bauhus, J. (2019). Predicting abundance and diversity of tree-related microhabitats in Central European montane forests from common forest attributes. Forest Ecology and Management, 432, 400–408.

Kahl, T., & Bauhus, J. (2014). An index of forest management intensity based on assessment of harvested tree volume, tree species composition and dead wood origin. Nature Conservation, 7, 15–27. https://doi.org/10.3897/natureconservation.7.7281

Knuff, A. K., Winiger, N., Klein, A. M., Segelbacher, G., & Staab, M. (2019). Optimizing sampling of flying insects using a modified window trap. Methods in Ecology and Evolution, 10(10), 1820–1825. https://doi.org/10.1111/2041-210X.13258

Müller, J., Mehr, M., Bässler, C., Fenton, M. B., Hothorn, T., Pretzsch, H., Klemmt, H. J., & Brandl, R. (2012). Aggregative response in bats: Prey abundance versus habitat. Oecologia, 169(3), 673–684. https://doi.org/10.1007/s00442-011-2247-y

Pereira, J. M. C., Schwegmann, S., Massó Estaje, C., Denter, M., Mikusiński, G., & Storch, I. (2024). Specialist carabids in mixed montane forests are positively associated with biodiversity-oriented forestry and abundance of roe deer. Global Ecology and Conservation, 50(April). https://doi.org/10.1016/j.gecco.2024.e02821

Storch, I., Penner, J., Asbeck, T., Basile, M., Bauhus, J., Braunisch, V., Dormann, C. F., Frey, J., Gärtner, S., Hanewinkel, M., Koch, B., Kuss, T., Pregernig, M., Pyttel, P., Reif, A., Gernot, M. S., Ulrich, S., Staab, M., Winkel, G., & Yousefpour, R. (2020). Evaluating the effectiveness of retention forestry to enhance biodiversity in production forests of Central Europe using an interdisciplinary, multi-scale approach. Ecology and Evolution, 10, 1489–1509. https://doi.org/10.1002/ece3.6003