https://doi.org/10.5061/dryad.hmgqnk9t9

Description of the data and file structure

This is the README file of the data published alongside a research paper

Contact: Anne Kempel (anne.kempel@slf.ch)

Paper title: Shifting heights? A 40-year resurvey of Alpine marmot distribution in response to climate change

First author: Miriam Simma

Co-authors: Arpat Ozgul, Francois Duchenne, Guido Ackermann, Hannes Jenny, Juerg Paul Müller & Anne Kempel

Journal: Ecology and Evolution

Year of publication: 2025

Files and variables

File: Simma_etal_fulldata_100x100.txt

Description. Alpine species are severely affected by climate change, with elevational range shifts being one key response of mountain species to the rapidly warming environment. The Alpine marmot (Marmota marmota) is suggested to be particularly susceptible to ongoing warming. However, it is largely unknown how climate change affected the Alpine marmot distribution in recent decades. In this study, we took advantage of historical marmot occurrence data collected in 1982 in the Dischma Valley, Swiss Alps, and resurveyed the marmot occurrences in the same area in 2022. During the last decades, temperature rises in Switzerland were twice as high compared to average temperature trends in the Northern Hemisphere. Moreover, since the 1970s, the mean annual maximum snow depth, the snow cover duration, and the number of days with snow on the ground has declined across the Swiss Alps. The aim of this study was to assess whether the Alpine marmot has colonized higher elevations in response to the rapid environmental changes during the last four decades. For this purpose, we used dynamic site-occupancy models based on the occurrence data from the two timepoints (1982 and 2022) to analyze how elevation, slope, aspect, and habitat type affected the initial marmot site-occupancies in 1982 and to estimate colonization and local extinction rates along the elevational gradient over time while accounting for imperfect detection. The dataset described in this README file contains the data of this study. More information can be found in the manuscript (link will be added upon publication).

Methodology: In a multi-species vertebrate survey in 1982, the distribution of the Alpine marmot was mapped in the Dischma Valley by recording marmot sightings within 25 defined observation areas across the valley. For this purpose, the researchers visited 25 selected viewpoints, each of them corresponding to a certain observation area (Figure 1), between July and September (Müller et al., 1986). Binoculars and telescopes were used to detect marmot individuals over large distances, which were up to 3602 m, from the viewpoint to the edge of the observation area (Figure S1, S2 in Supporting information). The survey durations at a single viewpoint ranged mainly between 1 and 2 hours, and viewpoints were mostly visited twice during the historical sampling. As ungulates were also recorded, most surveys were either conducted in the early morning or in the evening. The surveyed observation areas covered an area of 37.4 km2 and overall, they ranged from 1656 up to 3093 m asl. To assess the range of species occurrences as thoroughly as possible, the observers additionally carried out observation tours by foot in an unsystematic manner and recorded marmots sighted during the tours.

In July and August 2022, we revisited the 25 viewpoints and resurveyed the same observation areas for marmot occurrences. We recorded the location of sighted marmots on a digital orthophoto using the ArcGIS Survey 123 data collection app. To collect data comparable to the historical sampling design, we visited each viewpoint twice and surveyed the same areas for 1 - 2 hours. For each area, we conducted one survey in the first half of the day and one survey in the second half of the day. Midday heat was avoided, and no surveys were conducted on rainy days, as marmots are unlikely to appear above ground under those two weather conditions (Türk & Arnold, 1988). The unsystematic surveys carried out by foot in the past were not replicable, and hence, the resurvey in 2022 was restricted to the viewpoint visits.

The historical maps of the marmot records were georeferenced and digitised in 2022. We excluded marmot presence points, which were, according to the notes on the historical maps, indicated as being recorded during a tour by foot in the historical data to obtain comparable data with the ones from 2022.

The observation areas ranged between 27 and 280 ha in size. To define ‘sites’ as spatial units with potential marmot occupancy for the analysis, we subdivided the surveyed areas into 100×100 m grid cells, resulting in 3426 sites. Based on the point data of marmot detections in each survey (i.e., a sampling event occurring at time t at site i), we derived a detection history for each 100×100 m site. A detection history for dynamic site-occupancy models consists of 1 or 0 (detection/non-detection) for each site i and survey k (MacKenzie et al., 2003). Each observation area was surveyed twice in 2022; however, no survey-specific information (number of viewpoint visits, survey date) was available for the marmot presence points in 1982. Therefore, we considered the marmot presence in 1982 collectively as data from one (primary) survey, so there was no second survey to assess the detection probability. To account for imperfect detection in the past, the detection probability in the historical survey was assumed to be the same as - and thus estimated from - the repeated survey of 2022.

Variables and units of columns in data file

• site = x and y coordinates (x_y) according to the Swiss coordination system LV95 based on a 100 x 100 m grid.
• Survey1.82 = Presence or absence (0/1) of marmots in a grid cell of 100 x 100 m from the first survey in the year 1982
• Survey2.82 = there was no second survey in the year 1982, therefore these cells are empty
• Survey1.22 = Presence or absence (0/1) of marmots in a grid cell of 100 x 100 m from the first survey in the year 2022
• Survey2.22 = Presence or absence (0/1) of marmots in a grid cell of 100 x 100 m from the second survey in the year 2022
• elevation = mean elevation of the grid cell in m asl.
• slope = mean slope of the grid cell
• northness = mean northness of the gridded cell. This was computed as the cosine in radians, measured clockwise from north (Vandegehuchte et al., 2017). The derived index is a continuous variable termed ‘northness.
• vegetation = dominant type of vegetation in the grid cell, D = dwarf - shrubs, F = Forest and subalpine krummholz, G = alpine grasslands, meadows, pastures, and snow beds, O = other, S = scree, rocky habitats with sparse vegetation.
• dist.1 = distance from the observation viewpoint to the respected grid cell in m
• dist.2 = distance from the observation viewpoint to the respected grid cell in m
• dist.3 = distance from the observation viewpoint to the respected grid cell in m
• dist.4 = distance from the observation viewpoint to the respected grid cell in m
• elevation.2 = mean elevation of the grid cell in m

For further information, please contact the first or corresponding author or check the methods and supplementary information sections in the original publication.