Macroecological patterns of rodent population dynamics shaped by
bioclimatic gradients
Abstract
Long-term studies of cyclic rodent populations have contributed
fundamentally to the development of population ecology. Previous
research has shown macroecological patterns of population dynamics in
relation to latitude, but without disentangling the role of underlying
ecological and climate drivers. We collected 26 rodent time-series from
the tundra biome and assessed how population dynamics characteristics of
the most prevalent species varied with latitude and environmental
variables. While we could not find a relationship between latitude and
population cycle peak interval, other characteristics of population
dynamics had 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 population
cycle amplitudes in all species and ii) mid-winter climate variability
had negative impacts on cycle 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.
Keywords
Bioclimatic zones, climate, field vole, grey-sided vole, latitude,
macroecology, Norwegian lemming, population dynamics, tundra ecosystem,
tundra vole
Introduction
Studies of the cyclic population dynamics of small rodents have
contributed greatly to our understanding of population dynamics
(Berryman 2002, Stenseth 1999, Turchin 2003). In particular, long-term
series have provided an opportunity for macroecological studies
(Cornulier et al. 2013, Ehrich et al. 2020, Hansson and Henttonen 1985).
Such studies can reveal general patterns across large scales, enabling
comparisons of climatic and ecological drivers of population dynamics
and ecosystem functioning (Blackburn 2004, Kerr et al. 2007). Notably,
many of the previous large-scale analyses of Fennoscandian rodents have
shown strong latitudinal clines, with a northward increase in rodent
cycle peak interval and amplitude (Bjørnstad et al. 1995, Hanski et al.
2001, Hansson and Henttonen 1988, Korpela et al. 2013, but see
Angerbjörn et al. 2001). This gradient and its suggested connection to a
species richness gradient within the predator guild has found its way
into major ecology text books (Begon et al. 2006). Some studies based on
exceptionally long-term data have, however, indicated that such
macroecological pattern may be transient (Henden et al. 2009, Steen et
al. 1990).
In his book summarizing a century of research on rodent population
dynamics, Charles Krebs (2013) proposed to compile quantitative time
series to test macroecological hypotheses as a research agenda for the
next century. Examples of such hypotheses are the roles of food-web
interactions (Hanski et al. 2001, Oksanen et al. 2008), diseases (Burthe
et al. 2006), species-specific traits (Andreassen et al. 2013),
landscape configuration (Magnusson et al. 2015), and climate (Kausrud et
al. 2008, Tkadlec and Stenseth 2001) in shaping rodent dynamics.
Although surrogate variables such as latitude and altitude may provide
valuable clues about the underlying processes, quantification of more
mechanistic variables is needed to reach beyond pattern description
(Krebs 2013). Typically, several environmental variables change along
latitudinal and altitudinal gradients, making their respective effects
difficult to disentangle. For instance, previous studies of the
Fennoscandian latitudinal gradient have merged data from different
biomes (e.g. boreal forest and tundra) and the different rodent
communities specific to these biomes (Bjørnstad et al. 1995, Hanski et
al. 1991).
We propose that focusing on macroecological patterns of rodent
population dynamics within a single biome allows for stronger inferences
owing to less confounding between ecological and climatic variables. We
first assessed whether biome-specific analyses of Fennoscandian rodents
detected similar latitudinal patterns of population cycle
characteristics as previous studies. We focused on the tundra biome, as
rodent cycles have a particularly strong impact on tundra food-web
dynamics (Ims and Fuglei 2005). We then evaluated whether variables
describing winter climate variability and landscape composition would
give more insight to the observed patterns. We hypothesized that the
Norwegian lemming (Lemmus lemmus ), the only rodent species
endemic to the Fennoscandian tundra, is more sensitive to more variable
winter climate than other tundra-dwelling rodent species (Ims et al.
2011, Kausrud et al. 2008). Accordingly, we predicted that in regions
with more variable winter climate the lemming makes up a smaller
proportion of the rodent community and has lower amplitude cycles. We
further hypothesized that landscape structure has implications for both
rodent community structure and species-specific cycle amplitudes, as
maximum population growth rates and consequently highest amplitudes
should be related to high proportions of optimal habitat in the
landscape (Bondrup-Nielsen and Ims 1988, Dalkvist et al. 2011, Lidicker
2000). Accordingly, we predicted that different rodent species would
both dominate the rodent community and reach the highest cycle
amplitudes in the parts of the tundra biome where their primary habitats
occur.
Material and Methods
Study system
The tundra biome covers the arctic and oroarctic parts of Fennoscandia.
Despite substantial variation in climate, the relatively simple food web
has essentially a similar structure across the region. The low alpine
bioclimatic zone is predominantly dwarf-shrub tundra and the middle
alpine zone graminoid tundra (definitions according to Moen 1998, see
Table A3). These tundra types have similar vegetation composition
throughout the region, although the dwarf-shrub community has more
arctic features in the north (Virtanen et al. 2016).
We focused on the four most abundant and widespread rodent species in
the Fennoscandian tundra; besides the Norwegian lemming, the grey-sided
vole Myodes rufocanus and two ecologically closeMicrotus -voles (M. agrestis and M. oeconomus ,
considered here as one functional group), and refer to them as genera.
Based on food preferences (Soininen et al. 2013a), the low alpine zone
contains optimum habitats for grey-sided voles. Lemmings, in turn, reach
their highest numbers in the middle alpine zone (Ekerholm et al. 2001,
Ims et al. 2011), which is dominated by their preferred food plants,
i.e. graminoids and mosses (Soininen et al. 2013b). TheMicrotus -voles dwell in lush, moist, grass-rich habitats mainly
found as patches in the low alpine zone (Hansson 1969, Henden et al.
2011).
Time-series and spatiotemporal
replication
We collected time-series from 26 different locations in the
Fennoscandian tundra where lemmings occurred and where snap-trapping
data on all captured species were available for ≥ 10 years (Table 1,
Figures 1 and A1, Appendix 2). Some of the locations also included
trapping in adjacent ecotone forests (Appendix 2). The time-series at
the different locations have various degrees of spatial replication
(sampling units such as quadrats or trap lines ranging from 1 to
74 per location; Table 1). To link rodent population dynamics with
environmental variables, we focused on analyses at the sampling unit
level as the area extent of locations ranged from <1
km2 to >1000 km2(Appendix 2).
For analyses at the sampling unit level, we excluded sampling units if
i) trapping had been conducted less than 10 years, unless a new unit was
established in the vicinity to replace the previous unit, ii) the
time-series were disconnected by gap years resulting in shorter than 10
years of continuous data or iii) no rodents were ever trapped. We also
excluded locations from these analyses if data were not available per
sampling unit. In total, 22 locations (n = 385 sampling units) fulfilled
all criteria.
We used data from fall trapping season when available, as this is the
season included in most data series (Table 1). We assume that fall data
are more comparable between series than spring data, given that the
varying match between the timing of spring trapping and phenology likely
causes much noise in the data. For locations without fall trapping data,
we used data from spring trapping season (n=3 locations) or pooled data
across variable trapping dates (n=4 locations). To account for the
effect of sampling seasons we i) included sampling season as an additive
factor in all regression models, and ii) tested whether excluding
locations without fall data affected the results of the best models.
To make the time-series comparable, we used the number of rodents
captured per 100 trap nights per sampling unit as an abundance index in
all analyses.
Characteristics of rodent population
dynamics
We focused on characteristics of rodent population dynamics that have
consequences for ecosystem functioning, namely cycle amplitude ,peak interval, peak sharpness , mean densities andcommunity contribution (cf. Hanski et al. 1991, Henden et al.
2008, Krebs 2013, Turchin et al. 2000, Table A1). Variables and their
calculation are described in Table 2.
We assessed the presence of temporal trends in community contribution
(Text A4, Figure A12, Table A12). As no such trends were evident, we
proceeded with the approach of using rodent population cycle
characteristics aggregated over time as spatial replicates.
Environmental predictor
variables
We derived environmental predictor variables from raster data. It was
not a priori clear how large an area around a sampling unit best
predicts the local rodent numbers. We therefore extracted environmental
predictor variables at three spatial extents: 1 km2, 9
km2, and 25 km2 around each sampling
unit. Because the results differed only little, we present only the
largest extent (25 km2). We chose this extent because
it had the highest number of locations where any sampling unit had any
middle alpine zone within their buffers (n=3, 8, and 9, at 1
km2, 9 km2 and 25
km2, respectively). Results at other extents are given
in Text A2, Tables A8, A9, Figures A6, A7.
To assess winter climate impact on rodent population dynamics, we
extracted the long-term mean number of days in January-March when the
daily mean temperature was above zero. An increase in the number of days
would represent a more variable winter climate as the baseline is 0 or
very low number of days with temperature above 0oC
(i.e., stable “winter climate”). This metric was available for the
entire region and is linked to rodent winter demography (Aars and Ims
2002). We first created annual raster maps, depicting the number of days
in January-March with above-zero temperature. We chose this period
because we expect snow-covered conditions throughout the study area. The
annual maps were based on gridded daily mean temperature raster maps of
Fennoscandia, available from the Norwegian Meteorological institute,
Climatology Division (senorge.no). The daily maps are estimated by a
residual interpolation approach, applying terrain and other predictor
variables to define a trend that is removed from the observed
temperatures before they are interpolated into a 1 x 1 km gridded field.
The trend is then added to the interpolated field to obtain a spatially
continuous gridded temperature map (Tveito et al. 2005). Based on the
annual maps, we calculated a mean per sampling unit across a buffer zone
(5 km x 5 km) and the years when trapping was conducted at that unit.
To assess landscape composition, we used two approaches. First, we used
a map of tundra bioclimatic zones in Norway (Table A3, Moen 1998),
published by Blumentrath and Hanssen (2010). The map is based on
modeling the tree-line altitude and thereafter estimating the elevation
limits of the bioclimatic zones (Blumentrath and Hanssen 2010). The map
has pixel size 25m x 25m. For each sampling unit in Norway (n=221), we
extracted landscape composition by centering the sampling unit in the
middle of a 25 km2 (5 km x 5 km) square and
calculating the proportions of bioclimatic zones within the square.
Second, we used July mean temperature (°C) as a proxy of bioclimatic
zones, allowing inclusion of all locations (n=367 sampling units). We
used a temperature raster map of the July mean temperature for the
normal period of 1981-2010, available from the Norwegian Meteorological
institute, Climatology Division (Hanssen-Bauer et al. 2015). The map is
based on a residual interpolation approach as described for the winter
climate data. Within the bioclimatic zones, July mean temperature data
was distributed as follows (mean °C ± sd): low alpine (10.6 ± 1.3),
middle alpine (8.7 ± 1.5), and high alpine zone (6.8 ± 1.4) (Figure A4).
To extract the July temperature variable for each sampling unit, we
proceeded similarly as described for the winter climate data. As July
mean temperature was less than 50% correlated with the variable
describing winter climate variability (rho = 0.41), we proceeded to use
both variables in common models.
Statistical analyses of macroecological patterns in
rodent population
dynamics
We first assessed latitudinal patterns in the rodent population dynamics
characteristics. At the level of sampling unit, we constructed a linear
mixed effect model for each rodent genus and each characteristic, with
latitude and trapping season as fixed variables and location as a random
variable. As location-level data has previously been used to assess
latitudinal patterns (Bjørnstad et al. 1995, Hansson and Henttonen
1985), we also ran linear models of latitude impact on community
contribution and peak interval using location-level data.
We then assessed the effect of environmental variables on the population
dynamics characteristics, focusing on community contribution and
amplitude. We constructed two model sets: i) model set for all
data (n=385 sampling units from 22 locations) using July temperature
and winter climate variability as predictor variables, and ii)model set for Norwegian data (n=239 sampling units from 17
locations) using the proportion of optimal bioclimatic zone (low alpine
for voles and middle alpine for lemmings) as predictor variable instead
of July temperature. For each rodent genus and both model sets, we
included all additive combinations of relevant predictor variables,
together with trapping season as a fixed variable and location as a
random variable. Visual inspection of the data indicated a non-linear
effect of summer temperature for the two vole genera (i.e., temperature
optimum, Figure 3), and we therefore included a quadratic term of
temperature in these models. In all models for community contribution,
we log-transformed the response variable to achieve close to normal
distribution.
We assessed if, despite the large-scale synchrony in the occurrence of
rodent population peaks, there was spatial autocorrelation in the
population dynamics characteristics beyond the extent of location. To do
this, we assessed the evidence for a spatial autocorrelation of the
predicted random effects for location (Text A3, Figures A10, A11, Table
A10). When there was evidence for such autocorrelation, it could be
removed by including latitude as an additional covariate (Table A10),
and we checked if results were robust to the inclusion of latitude as a
covariate (Table A11). We selected the best models in each candidate
model set based on AICcc (Burnham and Anderson 2002).
Model selection was run with and without latitude as a covariate when
there was evidence for spatial autocorrelation.
All data analyses were done in the software R, using packages lme4
(linear mixed effect models, Bates et al. (2008)), AICcmodavg (AICc
based model selection, Mazerolle (2012)), and raster (extracting climate
data, Hijmans and Etten (2012)). We used 95% confidence intervals to
measure uncertainty for effects, and inspected model fit to assumptions
using diagnostic plots.
Results
Characteristics of rodent population
dynamics
At the sampling unit level, the community contributions of all three
rodents ranged from 0-100% (Table A2). However, lemmings andMicrotus were abundant in only few locations. The median of
community contribution across sampling units was > 50% in
two locations for lemmings and in three locations for Microtus ,
while the same was true for ten locations for grey-sided voles. At the
location level, community contribution of grey-sided voles and lemmings
ranged from almost absence (1-3%) to complete dominance (80-88%),
while Microtus reached at most 57% community contribution
(Figure 1b, Table A2).
Peak interval ranged from 2 to 13 years at the sampling unit level. The
very long maximum intervals arose from sampling units where a peak was
absent despite being present at other sampling units within the same
location. Consequently, peak intervals at sampling unit scale which were
longer than twice the mean across all units (i.e., > 8
years) were removed from the analyses. This resulted in a peak interval
range from 2 to 6.8 (mean 3.8 years, Table A2). Peak interval was less
variable at the location level than at the sampling unit level (ranging
from 3.2 to 4.7 with a mean of 3.9, Table A2).
At the sampling unit level, mean density was the population dynamics
characteristic with clearest differences between the rodent genera
(Figure 2, see Table A2 for all values in this paragraph and the
associated measures of uncertainty). Grey-sided voles mean densities
were on average higher than those of lemmings and Microtus (mean
across all sampling units; 1.3, 5.3, and 2.6 for lemmings, grey-sided
voles and Microtus , respectively). Grey-sided voles also had the
highest sampling unit specific mean densities, respectively two and five
times higher than for lemmings and Microtus . Amplitudes varied
less, although the mean across lemming amplitudes was slightly lower
than those of voles (2.3, 2.8, and 2.5 for lemmings, grey-sided voles,
and Microtus , respectively). In contrast, the mean across lemming
skewness was higher than those of voles (1.9, 0.5, and 1.3 for lemmings,
grey-sided voles and Microtus , respectively). This indicates that
lemming peaks were on average lower and sharper than vole peaks.
The characteristics of population dynamics were connected in all species
in a similar manner (Figure A3). High community contribution, high mean
density, high amplitude and low (below-zero or zero) skewness tended to
occur together, as did low community contribution, low mean density, low
amplitude and high (above-zero) skewness (Figure A3). This indicates
that independent of species identity, the dominant species in the rodent
community had high and round population peaks, whereas lower and sharper
peaks characterized less abundant species. However, lemming skewness
always remained above-zero (Figures 2, A3), indicating that sharp peaks
were a consistent characteristic of this species.
Latitudinal patterns of population
dynamics
The relationship to latitude differed between species (Figure 2, Table
3). Based on sampling unit specific analyses, the lemming community
contribution decreased northwards, but the other lemming characteristics
showed no latitudinal patterns. Grey-sided voles’ community contribution
increased northwards, as did their mean density and amplitude, whereas
their peak skewness decreased (i.e. peaks were less sharp). AlsoMicrotus’ mean density and amplitude increased northwards, but
less strongly than those of grey-sided voles (Figure 2, Table 3). The
mean density of the rodent community (i.e. all species combined)
increased northwards, but we found no latitudinal patterns in peak
interval. Location level patterns of community contribution were similar
to patterns at sampling unit level (Table A5, Figure A5). Location level
peak interval had no clear latitudinal trends, either. We explored
visually patterns between peak interval and other variables (location,
environmental variables, Figures A2, A9), but found no patterns.
Effects of climate and landscape on population
dynamics
The mean number of days with above-zero temperatures during
January-March ranged from 0.9 to 13.9 days per sampling unit, while July
mean temperature ranged from 7.6 to 12.7 °C (for all values in this
paragraph, see Table A4). Among the Norwegian locations where we had
data for alpine bioclimatic zones, low alpine tundra dominated
independent of spatial scale. Within a 25 km2neighborhood, low alpine zone made up an average of 81% (range 8-99%),
while mid alpine zone made up just 3% (range 0-43%). Furthermore, only
9 out of 17 Norwegian locations had sampling units with any middle
alpine zone within their buffers, while low alpine zone was present at
all locations. All variables were correlated with latitude; the
correlation was positive for July temperature and low alpine tundra, and
negative for the other variables (Figure A8).
For lemmings, high community contribution and high amplitudes were
related to the colder parts of the landscape (see Table 4 and Figure 3
for this and subsequent paragraphs). The model set with all data
indicated a negative effect of July temperature on both aspects of the
species population dynamics. The model set with only Norwegian data
supported this by indicating a positive effect of middle alpine zone on
community contribution. Winter climate variability was not included in
the best models for lemming community contribution, but it had a
negative effect on lemming amplitude.
For grey-sided voles, the different model sets indicated different
effects. The model set for all data related community contribution
positively to winter climate variability and amplitude positively to
July temperature. In contrast, the model set for only Norwegian data
related community contribution negatively to the optimal bioclimatic
zone and amplitude negatively to winter climate variability.
The Microtus community contribution was related to the
surrounding landscape. The model set for all data indicated a
negative effect of July temperature, with an increasing impact at higher
temperatures. The model set with only Norwegian data indicated a
positive effect of the proportion of low alpine zone. The results forMicrotus amplitude indicated a negative effect of high July
temperatures and a positive effect of the proportion of the low alpine
zone. Winter climate was not included in any of the best models forMicrotus . However, it was included in the second-best models, and
in the best models for amplitude at the most local scale (Tables A6,
A7).
Discussion
Our study is the first biome-specific macroecological analysis of a
rodent community at the scale of a biogeographic region (i.e.,
Fennoscandia). Earlier studies based on rodent trapping series from
Fennoscandia have been instrumental for demonstrating macroecological
latitudinal patterns of vertebrate community dynamics (Angelstam et al.
1984, 1985, Bjørnstad et al. 1995, Hanski et al. 1991, Hansson and
Henttonen 1985, Steen et al. 1990) and as baselines for generating
hypotheses of the underlying drivers of these dynamics (e.g. Hanski et
al. 1993, Hanski et al. 2001, Hansson and Henttonen 1988, Korpela et al.
2013, Korpela et al. 2014). Our approach enabled us to verify that the
previously found latitudinal gradient of the cycle amplitude was
also present within the tundra biome, albeit only found in voles and not
in lemmings. In contrast, we found no evidence for the previously found
northwards increasing peak interval (Bjørnstad et al. 1995, Hanski et
al. 2001). Further, the rodent community characteristics were related to
landscape composition. Thus, bioclimatic zonation appears to be a strong
predictor of structure and functioning of tundra rodent communities and
as such more informative than latitudinal gradients. Furthermore,
increasing winter climate variability decreased cycle amplitudes of both
lemmings and grey-sided voles, implying that impacts of a warming winter
climate may not necessarily be divergent between lemmings and voles as
we hypothesized. Some of the relationships we identified were
species-specific, demonstrating that lumping functionally different
species in analyses of population dynamics characteristics should be
done with great care, as it may mask relevant species-specific patterns.
Even the lumping of two ecologically similar Microtus species in
the present analyses may have affected our results. Taken together,
environmental variables provided new understanding beyond latitudinal
patterns.
Voles and lemmings have been described to have distinct shapes of the
cycles, with vole peaks being of lower amplitude and rounder than those
of lemmings (Turchin et al. 2000). Although we found lemming peaks to be
on average sharper than vole peaks, their cycle amplitudes were within
the same range as those of voles. Our results of cycle topology are thus
only partly in line with the hypothesis by Turchin et al. (2000), i.e.
different trophic interactions creating the different shapes of rodent
population cycles. Moreover, our results on cycle topologies may relate
to lemming peaks being observed less frequently than vole peaks, often
with no individuals during the low phase, which can result from the
scarcity of time-series sampling lemmings in their optimal bioclimatic
zone (middle alpine), or different trappability. The community
contribution of lemmings increased together with the proportion of
middle alpine bioclimatic zone of the landscape, conforming well to
smaller scale studies (Ekerholm et al. 2001, Ims et al. 2011, Kleiven et
al. 2018). We thus expect that time-series collected in the middle
alpine bioclimatic zone could have higher lemming cycle amplitudes than
those observed in the existing data. Comparing lemming data from the
middle alpine zone against vole data from the low alpine zone would
relate each species to their optimal parts of the landscape, and thus
provide a better case for comparing cycle topologies.
Our results matched only partly the earlier macroecological descriptions
of Fennoscandian rodent population dynamics (Angerbjörn et al. 2001,
Bjørnstad et al. 1995, Hanski et al. 1991, Hansson and Henttonen 1988,
Korpela et al. 2013). In particular, we found no support for the
latitudinal gradient in rodent population peak interval, unlike
Bjørnstad et al. (1995), and Hanski et al. (1991). A lack of patterns
within the tundra biome could indicate that this pattern arises from
comparisons between biomes (e.g. less variable peak intervals in the
tundra than in the boreal biome). Furthermore, it is unlikely that peak
interval remains fixed over several decades, given the variation
observed across Norway during the 20th century (Henden
et al. 2009, Steen et al. 1990). Our findings thus support the
conclusion of Henden et al. (2009); that the latitudinal gradient of
small rodent population dynamics characteristics in Fennoscandia is not
a temporally persistent phenomena and may rather be a case of transient
dynamics (Hastings et al. 2018).
The species-specific properties of population dynamics did, however,
show some of the same latitudinal patterns as described earlier (e.g.
Hanski et al. 1991, Hansson and Henttonen 1985). The cycle amplitude of
both vole species increased northwards, and the grey-sided vole
displayed a prominent northward increase of community contribution, mean
density, and cycle amplitude, concurrently with increasingly round
peaks. However, both the abundance of low alpine bioclimatic zone (for
Norway) and July temperature (for all locations) were positively
correlated with latitude. Thus, the latitudinal patterns of voles appear
to be related to an increasing abundance of the optimal bioclimatic zone
in the landscape (cf. Bondrup-Nielsen and Ims 1988, Lidicker 2000). An
increasing quality of the low alpine zone vegetation towards north, in
terms of increasing palatability (Virtanen et al. 2016), could also
contribute to explain this pattern.
Population dynamics of lemmings showed little latitudinal patterns,
except for a decreasing community contribution towards the north. On the
other hand, landscape composition appears to be a stronger predictor of
the species dynamics than latitude, as both cycle amplitude and
community contribution of lemmings had a positive relationship with the
colder parts of the landscape. Our findings thus support the idea that
landscape structure is an important determinant of both rodent community
structure (Ecke et al. 2017) and species-specific population dynamics
(Bondrup-Nielsen and Ims 1988, Le Vaillant et al. 2018, Lidicker 2000,
Magnusson et al. 2015, Pyke et al. 1977).
We found little evidence for winter climate impacts on lemming community
contribution. Yet, variable winter climate and community composition
appear related, as the frequency of above-zero winter temperatures was
positively associated with the community contribution of grey-sided
voles. Furthermore, population cycle amplitude of lemmings and
grey-sided voles decreased with increasing winter climate variability.
Andreassen et al. (2020) found a similar pattern in boreal forests,
relating the highest vole cycle amplitudes to the highest altitudes with
presumably the coldest winters. Similarly, Ruffino et al. (2016) found a
higher lemming peak in a continental study area with presumably colder
winter climate than in a coastal study area, but an opposite pattern for
grey-sided voles. Our results are thus only partly in line with these
more local studies, but it appears evident that winter climate
variability can affect both rodent community composition and the cycle
amplitudes of both voles and lemmings.
Lemmings have been suggested to be more sensitive to warm winter climate
than voles, potentially because their low-growing food plants can easily
be encapsulate by ice after melt-freeze events (Ims et al. 2008, Ims et
al. 2011). Our analyses did not reveal any dichotomy between lemmings
and grey-sided voles in terms of winter climate effects but provided
less evidence for the Microtus -voles sensitivity for winter
climate than for the other tundra rodents. These seemingly contrasting
results among the genera must, however, be interpreted with caution.
First, lemmings and Microtus voles were scarce in most locations
(n=5 and n=2 locations with more than 50% lemmings and Microtus ,
respectively), which potentially affected our ability to detect strong
winter climate impacts in these genera. Moreover, although within-year
spring and fall abundances are usually well correlated (Cornulier et al.
2013, Kausrud et al. 2008), winter climate is expected to have the most
direct impact on spring abundances. Indeed, previous local-scale studies
demonstrating impact of winter climate in Microtus and lemmings
were based on population growth rates between fall and spring (Aars and
Ims 2002). Thus, winter climate impacts are more likely to be revealed
by analyses of e.g., snap-trapping data from the spring or by camera
trapping data collected throughout the year. Furthermore, our variable
for winter climate was calculated from gridded meteorological data
across fixed mid-winter dates. Locally measured data on snow structure
would provide a more mechanistic variable (Domine et al. 2018, Kausrud
et al. 2008), while winter length can be decisive for cyclicity (Bierman
et al. 2006). Given the climate-change driven changes of snow conditions
(Pall et al. 2019) and the key role of rodents in tundra food webs (Ims
and Fuglei 2005), we encourage future studies to probe into the
mechanisms of snow condition impacts on rodent population dynamics.
Future perspectives
We propose that the Fennoscandian tundra and its rodent community are
well suited for further biome-specific macroecological studies. The
tundra biome extends across more than 10 latitudinal degrees in
Fennoscandia and covers distinct climate gradients (Moen 1998, Virtanen
et al. 2016), and small rodent dynamics in the tundra appear to have
more pervasive food web impacts than in other biomes (Ims and Fuglei
2005, Krebs 2011, Olofsson et al. 2012). We here show that in this
region, rodent population dynamics characteristics vary greatly within
the biome and between the rodent genera. More focused assessments of
causes of such variation have been called for (see Krebs 2013, Myers
2018), as most previous studies have been restricted to a few locations
and local context dependencies are therefore almost unknown (cf.
Soininen et al. 2018).
We see considerable scope for improvements for future macroecological
studies based on small rodent population time series from the
Fennoscandian tundra. Better insight may be achieved by i) extension of
small rodent monitoring to achieve greater representation of higher
alpine zones, ii) harmonization of practices and protocols, iii)
development of environmental predictor data layers across country
borders, iv) collection of data on tundra rodent dynamics throughout
different seasons (e.g. Mölle et al. 2021), and v) development of
predictor variables targeting winter climate impacts on rodents.
Long-term data in ecology is important in the face of anthropogenic
driven changes of land-use, climate, and contaminant loads (Berteaux et
al. 2017, Ecke et al. 2020, Ims and Yoccoz 2017). The scientific
community has recognized its importance (Haase et al. 2016, Lindenmayer
et al. 2012), but continued funding remains a challenge (Callaway et al.
2012). Yet, continued funding and increased coordination are
prerequisites to achieve an efficient macroecological study design. The
tundra biome is also the terrestrial biome on earth most affected by
climate change (Box et al. 2019, CAFF 2013, Post et al. 2009) and the
existing spatial configuration of population cycle characteristics is
likely to change accordingly. Monitoring of the tundra biomes’ key
players according to a macroecological protocol is a valuable approach
to detect the impacts of climate change on tundra ecosystem functioning.
Supplementary material
Appendix 1: additional figures, tables, and methodological details
Appendix 2: additional information on rodent-time-series
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