Birkebeinervegen monitoring (alias Birkebeinervegen rodent trapping).

Personnel: Over different years, the co-authors have led the sampling in the field and/or lab, with a yearly turnover of student participation.

Study area description: The study area is located in the southeast of Norway, in Innlandet Municipality (61°N, 11°E, Fig. 1). Here, arvicoline small rodents are known to exhibit cyclic population dynamics (Andreassen et al. 2020, Sonerud 2022) and the area is characterised by a semi-humid and continental climate. The study area is situated across an elevation gradient following the east-west orientated Birkebeiner Road with gradients in temperature, precipitation and vegetation (Table 1). The area is dominated by mixed coniferous forests with Norway spruce Picea albies and Scots pine Pinus sylvestris at low altitudes and more open areas with mountain birch Betula pubescens at higher elevations. The understoreys are dominated by dwarf shrubs, such as the bilberry Vaccinium myrtillus in the low elevations and lichens and grasses higher up.

Figure 1.  

Study area and the 23 trapping station locations (red circles) situated along the Birkebeiner Road. Each station contains a transect of 10 traps with fixed locations. The vegetation descriptions were taken close to the traps. The captures were further described and dissected at the University campus Evenstad (marked with a black dot).

Funding: This research was part of the BEcoDyn project supported by Hedmark University of Applied Sciences and a grant from the Norwegian Research Council (NFR project 221056) to H.P.A. Remaining funding from INN—Inland Norway University of Applied Sciences.

Study design: The Birkebeiner Road connects the two large valleys Gudbrandsdalen and Østerdalen from east to west over approximately 60 km (40 km straight line distance). The locations for the trapping stations were selected to obtain 100 m a.s.l. intervals between each station ranging from 300 m to 1,100 m a.s.l. (Fig. 1). This resulted in stations being between 0.5 and 4 km apart. This minimised the chances of trapping stations overlapping home ranges of small rodents.

In the initial setup for each trapping station, starting 10 m from the road, 10 metal snap traps (numbered 1-10, with trap 1 being closest to the road) were placed systematically 10 m apart along a transect at a right angle from the road. These trap locations were registered using handheld GPS devices (ca. 5 m accuracy), marked with coloured ribbons that were left out all year and their coordinates were reused the following years. The traps were placed in the understorey at the exact GPS location, however, slightly adjusted from 2022, when the traps were placed in the understorey in the most suitable location (i.e. close to holes, rock boulders, tree roots etc.) within one metre from the GPS location in order to maximise catches within the microsite.

Small mammal data: The trapping sessions consisted of five days of fieldwork. The trapping session was started by activating and baiting the traps with pieces of carrots mixed with peanut butter during the first morning. The three following mornings, all traps were checked and, if necessary, re-baited and/or re-activated. During these procedures, the trap’s status was noted (e.g. animal captured, broken trap etc., see a complete set of variables and definitions in the Data Resources section below in the DynamicProperties description in the Event dataset) and trapped animals were collected. On the last day of the session, traps were monitored as usual and retrieved from the field.

We carried out trapping sessions in autumn from years 2011 onwards and also in spring during the years 2011–2015. This translates into a total effort of 690 trap-nights per trapping session (see details in Table 2) with large inter-annual variation in the number of caught animals (Fig. 2, Table 2) and with the bank vole (Myodes glareolus) as the most common catch (87%).

Figure 2.  

The density index is estimated by the number of captured animals (grey) and captured bank voles Myodes glareolus (black) per 100 trap nights for annual autumn trappings (filled circle). The first years of trapping also included trapping during the spring season (+).

The collected animals were either brought into a laboratory immediately or frozen at -20^oC until dissection. Here, the animals were examined further including dissection for reproductive trait measures (see a complete set of variables and their description in Table 3). By using the variables eventID, occurrenceID or locationID, the datasets can be joined.

Vegetation data: Within a five-metre radius of each trap, we monitored the vegetation by describing the dominant habitat, tree layer, bush layer and field layer. We characterised these variables according to the descriptors given in Table 4. In 2020, each station’s habitat was described in further detail (Table 5), named with the prefix "ext" in the Extendedmeasurementorfact dataset.

Data availability: The data are available on Dataverse (DOI: https://doi.org/10.18710/OOJYQ0) and consist of three datasets that can be joined using the variables eventID, occurrenceID or locationID. The data and R script to ease download and import (including producing Fig. 2) are available at https://gitlab.com/becodyn/birkebeiner. Updates from future monitoring will be available on these services.

Quality control: We used standardised field forms to note observations which were followed by import to MS Excel (2011-2019 and 2021) and with predefined digital forms using KoboCollect (https://www.kobotoolbox.org/) from 2020 and onwards (with the exception of 2021) to reduce transcribing errors. The digital forms are available as .XML in the repositories and can be imported to KoboToolbox, ODK or similar services. Permits for trapping are given by The Norwegian Ministry of Climate and Environment.

Sources of error: Snap trapping provides only a relative density index. There are also local sources of error that potentially affect within and between-year values, such as: 1) trapping in various weather conditions affecting trapping success, 2) trap placement in more/less risky/suitable microhabitats, 3) trap placement in general could be an issue in spatio-temporal analysis, 4) molar teeth were not always checked on Microtus sp., thus there may be species level uncertainty between species identified as M. oeconomus and M. agrestis.

Description: Birkebeiner Road, Innlandet County, Norway.

61.460856 and 61.247073 Latitude; 11.023757 and 10.465131 Longitude.

The dataset in the current work is licensed under a Creative Commons Attribution (CC-BY) 4.0 Licence.