Data from: Macroecological patterns of rodent population dynamics shaped by bioclimatic gradients
Data files
Dec 23, 2024 version files 1.03 MB
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coordinates_locality.txt
1.28 KB
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peak_interval_locality.txt
590 B
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proportion_locality.txt
2.45 KB
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README.md
6.52 KB
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samp_unit_variables_all_series.txt
183.91 KB
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samp_unit_variables_norway_series.txt
127.10 KB
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time_series_data.txt
710.88 KB
Abstract
Long-term studies of cyclic rodent populations have contributed fundamentally to the development of population ecology. Pioneering rodent studies have shown macroecological patterns of population dynamics in relation to latitude and have inspired similar studies in several other taxa. Nevertheless, such studies have not been able to disentangle the role of different environmental variables in shaping the macroecological patterns. We collected rodent time-series from 26 locations spanning 10 latitudinal degrees in the tundra biome of Fennoscandia and assessed how population dynamics characteristics of the most prevalent species varied with latitude and environmental variables. While we found no relationship between latitude and population cycle peak interval, other characteristics of population dynamics showed latitudinal patterns. The environmental predictor variables provided insight into causes of these patterns, as i) increased proportion of optimal habitat in the landscape led to higher density amplitudes in all species and ii) mid-winter climate variability lowered the amplitude in Norwegian lemmings and grey-sided voles. These results indicate that biome-scale climate and landscape change can be expected to have profound impacts on rodent population cycles and that the macro-ecology of such functionally important tundra ecosystem characteristics is likely to be subjected to transient dynamics.
README: Macroecological patterns of rodent population dynamics shaped by bioclimatic gradients
https://doi.org/10.5061/dryad.h9w0vt4t6
Description of the data and file structure
This dataset is a collection of small rodent trapping datasets and spatial variables on climate and bioclimatic zonation derived for the trapping localities.
Files and variables
time_series_data.txt = time-series data used
each row represents trapping event per location/sampling unit
variables:
LOC = locality ID
year = year
season = trapping season (fall or spring)
samp_unit = sampling unit ID
trapn = number of trapnights
Mruf = number of grey-sided voles (Myodes rufocanus)
Mrut = number of red-backed voles (Myodes rutilus)
Magr = number of Field voles (Microtus agrestis)
Llem = number of Norwegian lemming (Lemmus lemmus)
Msch = number of Wood lemmings (Myopus schisticolor)
Mgla = number of bank voles (Myodes glareolus)
Moec = number of tundra voles (Microtus oeconomus)
Asyl = number of Wood mouse (Apodemus sylvaticus)
vole_ssp = number of vole individuals not specified to species
- variables ending with ind are trapping index number of individuals per 100 trapnights
samp_unit_variables_all_series.txt = data per sampling unit, both population dynamics variables derived from time-series data and spatial predictor variables. dataset for all data.
variables
LOC = locality ID
samp_unit = sampling unit ID
easting = longitude coordinate UTM 33
northing = latitude coordinate UTM 33
start_year = first year of dataseries
end_year = last year of dataseries
season = trapping season included in the analyses
prop_Mruf = proportion (community contribution) Myodes rufocanus
prop_Llem = proportion (community contribution) Lemmus lemmus
prop_Microtus = proportion (community contribution) Microtus
prop_other = proportion (community contribution) other species
Mruf_mean = mean number of Myodes rufocanus
Llem_mean = mean number of Lemmus lemmus
Microtus_mean = mean number of Microtus
Com_mean = mean number of all species
Mruf_amp = amplitude (s-index) Myodes rufocanus
Llem_amp = amplitude (s-index) Lemmus lemmus
Microtus_amp = amplitude (s-index) Microtus
Com_amp = amplitude (s-index) entire rodent community
Mruf_skew = skewness Myodes rufocanus
Llem_skew = skewness Lemmus lemmus
Microtus_skew = skewness Microtus
Com_skew = skewness entire rodent community
median_interval = median population peak interval entire rodent community
mean_interval = mean population peak interval entire rodent community
sd_interval = standard deviation of population peak interval entire rodent community
MEAN_DAYSABOVE_3km = mean number of days in January-March when the daily mean temperature was above zero, calculated for a 9sqkm buffer zone
SD_DAYSABOVE_3km = standard deviation of days in January-March when the daily mean temperature was above zero, calculated for a 9sqkm buffer zone
MEAN_DAYSABOVE_5km = mean number of days in January-March when the daily mean temperature was above zero, calculated for a 25sqkm buffer zone
SD_DAYSABOVE_5km = standard deviation of days in January-March when the daily mean temperature was above zero, calculated for a 25sqkm buffer zone
MEAN_DAYSABOVE = mean number of days in January-March when the daily mean temperature was above zero, calculated for a 1sqkm buffer zone
SD_DAYSABOVE = standard deviation of days in January-March when the daily mean temperature was above zero, calculated for a 1sqkm buffer zone
july_temp_1km = mean July temperature, calculated for a 1sqkm buffer zone
july_temp_3km = mean July temperature, calculated for a 9sqkm buffer zone
july_temp_5km = mean July temperature, calculated for a 25sqkm buffer zone
samp_unit_variables_Norway_series.txt = data per sampling unit, both population dynamics variables derived from time-series data and spatial predictor variables. dataset for Norwegia sseries only. List of variables is the same as in the datafile for all data, except for:
low_alpine_1km = proportion of low alpine bioclimatic zone in a 1sqkm buffer zone
middle_alpine_1km = proportion of middle alpine bioclimatic zone in a 1sqkm buffer zone
high_alpine_1km = proportion of high alpine bioclimatic zone in a 1sqkm buffer zone
low_alpine_3km = proportion of low alpine bioclimatic zone in a 9sqkm buffer zone
middle_alpine_3km = proportion of middle alpine bioclimatic zone in a 9sqkm buffer zone
high_alpine_3km = proportion of high alpine bioclimatic zone in a 9sqkm buffer zone
low_alpine_5km = proportion of low alpine bioclimatic zone in a 25sqkm buffer zone
middle_alpine_5km = proportion of middle alpine bioclimatic zone in a 25sqkm buffer zone
high_alpine_5km = proportion of high alpine bioclimatic zone in a 25sqkm buffer zone
coordinates_locality.txt = locality-specific coordinates, northing and easting in UTM 33
peak_interval_locality.txt = locality-specific peak interval
proportion_locality.txt = community contribution at locality Level
variables:
LOC = locality
SEASON = trapping season included in the analyses
prop_Mruf = proportion (community contribution) Myodes rufocanus
prop_Llem = proportion (community contribution) Lemmus lemmus
prop_Microtus = proportion (community contribution) Microtus
prop_other = proportion (community contribution) other species
Code/software
All data analyses were done in the software R, using packages lme4 and AICcmodavg. The file time_series_data.txt has the rodent trapping data that was used to calculate population dynamics variables per sampling unit (files having "sampling_unit" in the filename) and location (files having "locality" in the file name).
Access information
Data was derived from the following sources:
- Rodent trapping time-series were acquired from the authors.
- July temperature data was derived from a temperature raster map of the July mean temperature for the normal period of 1981-2010, available from the Norwegian Meteorological institute, Climatology Division
- Winter climate data was derived from gridded daily mean temperature raster maps of Fennoscandia, available from the Norwegian Meteorological institute, Climatology Division (senorge.no).
- the bioclimatic zonation was derived from a map of tundra bioclimatic zones in Norway, published by Blumentrath and Hanssen (2010) Beregning av areal -In: Nybø (ed) Datagrunnlaget for ”Naturindeks i Norge 2010”, DN-utredning 4-2010 (in Norwegian).
Methods
This dataset is a collection of rodent time-series from Fennoscandia, together with spatial variables on climate an bioclimatic zonation.