DOI: 10.6084/m9.figshare.13860167.v2
title: Data for "Differential spatial responses of rodents to masting on forest sites with differing disturbance history"

description: <b>Study area</b><br><br>The Dürrenstein Wilderness Area (DWA; 47°48’ to 47°45’ N, 15°01’ to 15°07’ E) is located within the northern Limestone Alps of Lower Austria, Austria. The protected area of the DWA covers 3500 ha in total from which approximately 400 ha are declared as a strictly protected area (International Union for Conservation of Nature category Ia) due to the primeval forest Rothwald which has never been logged (Kral & Mayer, 1968; Splechtna & Splechtna, 2016). A smaller part of the primeval forest is located in a basin, which is surrounded by steep slopes. <br>We established five study sites for small mammal trapping (Table 1). Three study sites are situated at the southern slopes of the mountain Dürrenstein (1878 m a.s.l., 47°47’ N, 15°04’ E). The two study sites within the basin of the area are about 4000 m linear distance in southeastern direction to the summit of Dürrenstein. The primeval forest in the basin (PFb) is characterized by a high amount of dead wood and densely mixed regeneration and ground vegetation cover. The managed forests in the basin (MFb) are dominated by <i>Picea abies</i>, intermixed with <i>Fagus sylvatica</i> and <i>Abies alba</i>. Ground vegetation is dominated by <i>Vaccinium myrtillus</i> which is less common at all other sites. The managed forest (MFb) is adjacent to Rothwald and other beech-dominated forest stands. The primeval forest at the slopes (PFs) is characterized by old individuals of <i>F. sylvatica</i>. Regeneration is patchily distributed and ground vegetation cover is less compared to PFb. Calcareous C-horizon is partly exposed and offers many holes accessible for small animals.<br>A windthrow in 1990 formed a large gap in the forest stand close to the primeval forest Rothwald. The area was not cleared from logs and provides a heterogeneous habitat with dense thickets intermixed with patches of grassland (WTs). In 2009, an avalanche created a disturbance of 10.1 ha size with a length of over 1,000 m and a width of up to 120 m (AVs). <i>P. abies</i> established rapidly in the middle parts, while the avalanche runout zone is characterized by a high amount of coarse woody debris and gravel. Eleven years after the disturbance event open grassland occupies around half of the site (Brenn, 2018).<br><br><b>Data acquisition</b><br><br>Live-trapping of small mammals<br>To estimate abundance of small mammals, we conducted live trapping between 2004-2019. Trapping sessions were carried out between May to October with one to three sessions each year. The duration of a trapping session varied between 2-5 consecutive trap nights. Trapping grids where composed of 5x5 trap stations arranged on a grid with 15m distance between stations. Accounting for the elongated shape of the avalanche site we used a modified grid design there, placing 44 trap stations on three sub-grids while maintaining trap distance. We placed two traps of different manufacturers (i.e. wooden box traps, Sherman Traps, Tube Traps and Trip Traps) at each trap station and covered them with vegetation or other organic material to mitigate extreme temperatures. We baited each trap with butter cookies, peanut butter, and a piece of apple (Cody & Smallwood, 1996). Traps were checked each morning and set active in the evening. Species or genera were identified according to (Niethammer & Krapp, 1978, 1982). Owing to the high protective status of the research area, we were unable to use artificial permanent marking methods such as passive integrated transponder tags or metal tags that would otherwise accumulate in the forest. All field work was conducted in accordance with the reserve administration and the scientific advisory board of the Dürrenstein Wilderness Area and permits by the Government of Lower Austria, Nature Conservation Division (RU5). <br><br>We summarized live-trapping data for the most dominant taxa (<i>Myodes glareolus</i>, <i>Apodemus</i> spp.) as the number of captured individuals ‚C<sub>ij</sub>‘ within a trap-night ‚j‘ at session ‚i‘. In summary, the data contains 115 sessions, capturing 455 nights of trapping.<br><br>Seed Rain<br>To estimate spatiotemporal differences in food availability for granivorous rodents, we monitored seed rain at the slopes and at the basin of the study area. We established two plots using geostatistical grid designs with 81 seed traps in 2003 and from 2006 – 2018. The traps consisted of plastic troughs with a basal area of 0,24 m2 covered in wire mesh to prevent further dispersal or predation of seeds. We emptied all seed traps in early spring right after snow melt and additionally during late October/November unless unpredictable snow cover prevented us to enter the area. Collected seeds were separated from leaf litter and other organic material and counted for each tree species and seed trap. Seeds fallen between late summer and the following spring were summed up for each seed trap as annual estimates and we used log-transformed mean values of 81 seed traps and scaled the number of seeds to [seeds/m2] for further analysis. We combined the number of seeds of <i>P. abies</i> and <i>A. alba</i> (hereinafter referred to as conifer seeds), as their seed rain was positively correlated.<br><br>Microclimate<br>To account for microclimatic variation among plots and trap nights, which can influence the activity and capture numbers of <i>A. flavicollis</i> and <i>M. glareolus</i> in a species-specific manner (Wróbel & Bogdziewicz, 2015), we used a fine-scale model (Kearney et al., 2020) to estimate hourly mean temperatures for each study site using the R-package microclima (Maclean et al., 2019). We further obtained estimates of daily precipitation using the function ‘microclimaforNMR’ of the same R-package (Kearney & Porter, 2017).<br><br>We estimated hourly values for aboveground temperature at each study site by using the function ‚runauto‘ from the R-package ‚microclima‘ (Version 0.1, Maclean et al., 2019). We prepared polygons of the study sites and added a buffer of 50 m around each site. Subsequently, we used these polygons to clip a digital terrain model with a spatial resolution of 30m which then served as input for the function ‚runauto‘. We specified the habitat type for each site as follows: managed forest MFb as ‘Evergreen needleleaf forest’, primeval forests PFb and PFs as ‘Deciduous broadleaf forest’, the windthrow site WTs as ‘Closed shrublands’ and the avalanche site AV as ‘Open shrublands’. We ran the function for each site and each year separately and obtained estimated temperature values at a height of 0.1 m aboveground with a spatial resolution according to our digital terrain model at an hourly interval. We converted the timezone from Coordinated Universal Time into Central European Time and aggregated the temperature values for each timestep and site to obtain an hourly mean temperature value for each site. We further summarized the mean temperatures between 7 pm and 6 am in order to obtain nightly temperature estimates that correspond to the time of the day when small mammal live traps where set active.<br><br><b>Data description</b><br><br>- each column represents a trap night (only sheets 1-5)<br>- each row represents a unique combination of site and session (session = up to 5 trap nights)<br><b>References</b><br><br>Brenn, M. E. (2018). Baumverjüngung nach einer Schneelawine in Bergurwald-Ökosystemen: Am Beispiel des Urwaldes Rothwald [PhD thesis]. University of Natural Resources; Life Sciences, Vienna.<br><br>Cody, M. L., & Smallwood, J. A. (Eds.). (1996). Long-term studies of vertebrate communities. Academic Press.<br><br>Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P., & Maclean, I. M. D. (2020). A method for computing hourly, historical, terrain-corrected microclimate anywhere on earth. Methods in Ecology and Evolution, 11(1), 38–43. https://doi.org/10.1111/2041-210X.13330<br><br>Kearney, M. R., & Porter, W. P. (2017). NicheMapR an R package for biophysical modelling: The microclimate model. Ecography, 40(5), 664–674. https://doi.org/10.1111/ecog.02360<br><br>Kral, F., & Mayer, H. (1968). Pollenanalytische Überprüfung des Urwaldcharakters in den Naturwaldreservaten Rothwald und Neuwald (Niederösterreichische Kalkalpen). Forstwissenschaftliches Centralblatt, 87(1), 150–175. https://doi.org/10.1007/BF02735860<br><br>Maclean, I. M. D., Mosedale, J. R., & Bennie, J. J. (2019). Microclima: An r package for modelling meso- and microclimate. Methods in Ecology and Evolution, 10(2), 280–290. https://doi.org/10.1111/2041-210X.13093<br><br>Magalhães, J. P. D., & Costa, J. (2009). A database of vertebrate longevity records and their relation to other life-history traits. Journal of Evolutionary Biology, 22(8), 1770–1774. https://doi.org/10.1111/j.1420-9101.2009.01783.x<br><br>Niethammer, J., & Krapp, F. (1978). Handbuch der Säugetiere Europas. Nagetiere I. Aula-Verlag.<br><br>Niethammer, J., & Krapp, F. (1982). Handbuch der Säugetiere Europas. Nagetiere II. Aula-Verlag.<br><br>Splechtna, B. E., & Splechtna, K. (2016). Rothschild’s wilderness: How a primeval forest survived the timber industry. Arcadia: Explorations in Environmental History, 4. https://doi.org/10.5282/rcc/7420<br><br>Tacutu, R., Thornton, D., Johnson, E., Budovsky, A., Barardo, D., Craig, T., Diana, E., Lehmann, G., Toren, D., Wang, J., Fraifeld, V. E., & de Magalhães, J. P. (2018). Human Ageing Genomic Resources: New and updated databases. Nucleic Acids Research, 46(D1), D1083–D1090. https://doi.org/10.1093/nar/gkx1042<br><br>Wróbel, A., & Bogdziewicz, M. (2015). It is raining mice and voles: Which weather conditions influence the activity of Apodemus flavicollis and Myodes glareolus? European Journal of Wildlife Research, 61(3), 475–478. https://doi.org/10.1007/s10344-014-0892-2<br>