Skip to main content

Sustained population decline of rodents is linked to accelerated climate warming and human disturbance

Abstract

Background

During the past three decades, sustained population decline or disappearance of cycles in small rodents have been observed. Both anthropogenic disturbance and climate warming are likely to be potential drivers of population decline, but quantitative analysis on their distinct effects is still lacking.

Results

Using time series monitoring of 115 populations (80 populations from 18 known rodent species, 35 mixed populations from unknown species) from 1980 in China (spanning 20–33 yrs), we analyzed association of human disturbances and climate warming with population dynamics of these rodent species. We found 54 of 115 populations showed a decreasing trend since 1980, and 16 of 115 showed an increasing trend. Human disturbances and climate warming showed significant positive associations with the population declines of most rodent species, and the population declines were more pronounced in habitats with more intensified human disturbance such as cities and farmlands or in high-latitude regions which experienced more increase of temperature.

Conclusions

Our results indicate that the large-scale sustained population decline of small mammals in various ecosystems driven by the rapid increase of both climate warming and human disturbance is likely a signal of ecosystem dysfunction or transition. There is an urgent need to assess the risks of accelerated climate warming and human disturbance imposes on our ecosystems.

Peer Review reports

Background

Global average temperature of the earth has increased approximately 1.09 °C in the past 150 years, which has accelerated the melt of glaciers and ice since the 1980s [1, 2]. At the same time, human disturbance such as logging, grazing, and farming has caused extensive damage to our ecosystems. Under the impacts of accelerated global change, 16–33% vertebrates are globally threatened or endangered [3]. In response to climate warming, the ranges of many terrestrial animals shifted toward higher latitude or elevation area [4, 5]. Climate warming and human disturbance were thought to be the causative drivers of the large-scale population decline of endangered species [6].

Small rodents make up approx. 42% of mammalian species. Many rodent species are pests to human society because they cause damage to our agriculture, forestry, grasslands, and transmit many diseases to people [7]. However, they also play an important role in maintaining ecosystem function and services [8]. As keystone species, rodents are preyed upon by many predators [9], and play important roles in plant seedling regeneration as seed dispersers [10]. They also response quickly to global change due to their short lifespan and high reproductive capacity, and are often used as an important indicator of ecosystem function considering their large abundance, high diversity and wide distribution. Sustained decline of populations and collapse of communities of small rodents may be an early signal of ecosystem dysfunction or transition.

Population abundance of many small rodents species oscillate greatly from year to year under influences by both extrinsic and intrinsic factors [11, 12]. Recent studies found that the population cycles of voles and lemmings in Europe are collapsing or disappearing [13,14,15,16,17], and population of common hamsters has declined 74% since 1970s in Europe [18], likely caused by climate warming or human disturbance. However, a few other studies argue against climate warming as the cause for the loss of cycles [14, 19]. Thus, it is questionable if the observed population decline is a global phenomenon or a regional one. Because climate warming is closely associated with increased human disturbances during past decades, it is also necessary to distinguish the distinctive effects of human disturbance and climate warming in causing population declines of small rodents.

The purpose of this study aims to evaluate associations of climate warming and human impacts with population dynamics of small rodents in China by using 115 time series of historical data (spanning 20–33 yrs) since 1980 based on the literature. Because rodents are important pests in many ecosystems of China, they are well monitored or studied by governmental agencies or research institutions. The time series of our study covers various ecosystems and climate zones across China, which experienced different pressures of climate warming and human impacts, thus, provide an opportunity to disentangle their distinct effects on population dynamics of small rodents.

Results

Changing trend of rodent abundance

In this study, 115 time series (80 populations from 18 known rodent species, 35 mixed populations from unknown species) with durations ranging from 20 to 33 years (from 2737 datum) since 1980 were reconstructed, covering a broad range and various ecosystems of China (Fig. 1, Additional file 1: Figs. S1, S2, Table S1).

Fig. 1
figure 1

Spatial distribution of 115 time series (80 populations from 18 known rodent species, 35 mixed populations from unknown species) of rodents in China and the changing trend of 18 rodent species. The pies in the map show the sites of the 115 time series, the size of the pies indicate the number of time series, and the size of slices of each pie indicate the proportion of increase (red), decrease (blue), and no trend (green) time series. The surrounding panels around the map show the changing trend of normalized rodent abundance of the 18 rodent species and the mixed populations. The color of symbols or lines indicates the changing trend of the time series: increase (red), decrease (blue), and no trend (green). The dashed gray lines of each panel indicate the rodent abundance of each species from multiple sites

Nearly half of the time series (54 of 115) showed decreasing trend since 1980, but only 13.9% (16 of 115) showed increasing trend, while others did not show any significant trend (Fig. 2A, Fig. 3, Additional file 1: Table S2, S3). For the 16 populations showing increasing trend, they also showed obvious population decline since 2000 (Fig. 3B). Most of the annual temperatures of the study sites showed increasing trend (Fig. 2B), and all GDP showed accelerated increasing trend (Fig. 2D), while most precipitation time series showed no trend, only a few showed decreasing trend (Fig. 2C).

Fig. 2
figure 2

Normalized time series of rodent abundance, temperature, precipitation, and GDP of each location. A Annual rodent abundance of 115 populations (the same below); B annual mean temperature; C annual precipitation; D prefectural annual gross domestic product (GDP). Line color indicates the changing trend of rodent abundance and environmental variables for a given population (red: increase; blue: decrease; grey: no trend). Black solid line indicates loess regression with span = 0.25

Fig. 3
figure 3

Changing trend of rodent abundance (normalized) of 115 time series of rodents in China. A Pooled populations, B increase populations, C decrease populations, D no trend populations. Grey solid lines represent normalized rodent population dynamics; Blue solid line represents 50% moving kernel-smoothed quantile, upper and down blue dashed line represent 90% and 10% quantiles. Pooled population means all populations including 35 mixed populations and 80 populations of 18 single species. Wavelet analysis for the population cyclicity of pooled populations (panel A) was shown in Additional file 1: Fig. S3

The pooled time series of rodent abundance showed a steady decline, with two obvious population peaks at 1988 and 1996 and rapid population decline since 1996 (Fig. 3A). Increasing populations show increase since 1980’s but decline since 2000 (Fig. 3B); decreasing populations showed a consistent decline since 1980 (Fig. 3C). The pooled rodent abundance showed a significant periodicity at 2–4 years, nonsignificant at 4–12 years during 1980–1987, but the amplitude of population cyclicity showed decrease for periodicity at 2–4 years after 1997 and at 4–12 years after 1990 (Additional file 1: Fig. S3).

Correlation analysis indicated that GDP showed significant and negative correlations with population abundance of pooled population, mixed populations and 10 of the known species, but positive correlation with two species (Bandicota indica and Spermophilus alashanicus). Temperature showed significant and negative correlations with pooled population and 6 species, but positive correlation with one species (Spermophilus alashanicus). Precipitation showed a significant and positive correlation with population abundance of the pooled populations and one species (Rattus tanezumi) (Additional file 1: Fig. S2, Table S4).

Using GAM analysis, city/farmland habitat and latitude showed significant and negative effects on the changing trend of rodent abundance (p < 0.001 for city/farmland habitat; p < 0.01 for latitude), whereas body mass did not show significant effect on the changing trend (p > 0.05).

Association of human disturbance and climate with rodent abundance

Using spatial–temporal general additive model (GAM) analysis, the association of climate change and human disturbance with rodent abundance were analyzed for each species (Table 1). We found the rodent abundance of last year showed significant and positive associations with rodent abundance of the pooled populations, mixed populations and 18 known species (except for Rhombomys opimus, Niviventer confucianus, Apodemus peninsulae, Rattus nitidus, and Phodopus sungorus). GDP showed significant and negative associations with rodent abundance of the pooled population and 7 known species including Rattus norvegicus, Apodemus agrarius, Mus musculus, Meriones unguiculatus, Rattus losea, Rattus nitidus, and Tscherskia triton (Table 1).

Table 1 Associations of rodent abundance with the intrinsic factor (rodent abundance of last year), human disturbance factor (annual GDP), climate factors (annual mean temperature, annual precipitation), habitat, and spatial autocorrelation based on analyses using Eq. (1)

Temperature showed significant and negative associations with rodent abundance of the pooled population, mixed populations and 3 known rodent species including Rattus norvegicus, Apodemus agrarius, and Apodemus peninsulae in the current year or with one-year time lag; only one significant and positive association was found for one species (Spermophilus alashanicus) with 1-yr time lag (Table 1). Precipitation showed significant and positive associations with rodent abundance of the pooled populations and Cricetulus barabensis in the current year or with one-year time lag (Table 1).

Habitat type showed significant association with rodent abundance of the pooled populations and 4 known species including Rattus norvegicus, Apodemus agrarius, Mus musculus, and Cricetulus barabensis, and spatial auto correlation showed significant association with rodent abundance of the pooled populations and Mus musculus (Table 1).

Using GAM analysis, the effects of body mass, habitat, and latitude on the association coefficients between population abundance and variables of climate change and human disturbance were analyzed (Additional file 1: Table S5). We found 4 habitat types (grassland—farmland mosaic, city, farmland, and forest) showed negative effects on the associations coefficients between GDP on rodent abundance, with the order of city > farmland > grassland—farmland mosaic = forest (Additional file 1: Table S5). Two habitat types (city and farmland habitat) showed negative effects on the association coefficients between temperature and rodent abundance (Additional file 1: Table S5).

Discussion

By analyzing multiple long-term time series of rodent abundances across different ecosystems and climate zones in China, we demonstrated that about half of the rodent populations showed sustained decline during the past three decades, and both climate warming and human disturbance were linked to the population decline of rodents in China; such negative association with population abundance of rodents were more pronounced in habitats with intensified human disturbance or in high-latitude regions which experienced more climate warming. Besides, we found most populations showed significant density-dependency effects, and precipitation showed positive effects on rodent abundance of many species. Our results suggest that the observed large-scale population decline of small mammals is likely a global phenomenon driven by intensified human disturbance and climate warming, and rodent population in intensified human disturbance habitats such as city and farmland were more vulnerable. The observed population decline is likely a signal that our ecosystems is heavily disrupted by both human disturbance and climate warming. Under the on-going accelerated global change, we predict that the previously predominant rodent species will become less abundant, and will be replaced with other less abundant species or invaded species, resulting in ecosystem transitions or dysfunction.

Effect of human disturbances

Large mammals are facing population decline or local extinction under intensified human disturbances, with 322 terrestrial vertebrate species going extinct during past 500 years [3]. In contrast to large mammals, small rodents are much more resistant to human disturbances due to their large populations, high reproductive rates, high dispersal rate but small body size [20].

Previous studies indicate that moderate human disturbance could benefit rodent species by providing favorable conditions to trigger population increases. For example, livestock grazing significantly increases rodent abundance and their damage to the arid grasslands of Inner Mongolia [21] and meadow ecosystems in the Qinghai-Tibetan plateau [22] in China; this is because grazing reduces the grass height, which benefits small rodents in the grassland that favor open habitats for social communication or more alertness to predation.

However, over-disturbance by human activities may reverse its positive effects on rodents. Zhang et al. found the population increase rate of Brandt’ voles had a dome-shaped response: too low or high vegetation cover reduced the increase rate, suggesting over-grazing would impose negative impact on small rodents in grasslands [23]. By manipulation of grazing in large enclosures in Inner Mongolia, Li et al. demonstrated that successive grazing significantly reduced population abundance of the voles by decreasing the food quality and quantity, which explains why the voles became rare in most part of Inner Mongolia during the past two decades [24]. In Europe, the population declines of hamsters was found to be linked to intensified and monoculture farming [13,14,15,16,17,18].

In this study, we found GDP, the only available indicator of human disturbances in the study sites, showed significant and negative associations with pooled populations and populations of nearly half (7 out of 18) of the rodent species, indicating that the intensified human disturbance during the past three decades reversed its positive effect on rodents under moderate human interferences. Meanwhile, negative effects of temperature and GDP on rodent populations were more pronounced in habitats with more intensified human disturbances such as city or farmland, further supporting the observation that accelerated human disturbance showed negative effects on population of rodents. Of the species showing sustained population decline and were negatively affected by GDP, they also mainly inhabited in the heavily disturbed areas nearby humans and consume anthropogenic foods, including Rattus norvegicus, Apodemus agrarius, Mus musculus, Rattus losea, Rattus nitidus and Tscherskia triton living in farmlands and villages. Niviventer confucianus mainly dominated in deforested areas, while Meriones unguiculatus dominated in heavily grazed areas (e.g. converted into semi-desert habitats) in grasslands. Human disturbance showed non-significant effects on the other species mostly live in habitats where human disturbance is less extensive.

It is notable that GDP may not represent the effect of other human disturbance, such as rodent control, or land use. Rodents have been extensively controlled by using various methods such as rodenticides, traps, or habitat management, which may contribute to the population decline of some rodent species, and in some cases, they may outweigh the positive effects of climate [25]. In city or village area, with the development of economy as represented by GDP, the living condition has been well improved due to increased sanitation measures, extensive baiting program, and adaptation of concrete building, which significantly suppressed population density of rodents.

Effect of temperature

It is generally thought that a warmer climate would benefit rodent populations by increasing plant growth (for their food resources) or winter survival of rodents or early breeding, but shorter winters may dampen lemmings and vole’s populations cycles in Arctic area [26,27,28]. Pucek et al. found high summer temperature of the previous year triggered seed masting of trees, and then population outbreaks of rodents in the following year [29], consistent with those findings, the positive association between climate warming and population abundance was also found for Spermophilus alashanicus likely due to increase of vegetation under climate warming [30, 31].

In contrast with results of previous studies, our study demonstrated that temperature showed significant and negative associations with the pooled population, mixed population and populations of 4 known rodent species, including Rattus norvegicus, Apodemus agrarius, Apodemus peninsulae, and Marmota sibirica, which are from diversified habitats including farmland (A. agrarius, R. tanezumi), human settlement (R. norvegicus), forest (A. peninsulae, agrarius), grassland (M. sibirica). Recent studies also found temperature showed a negative association with marmots in Subei County of northern China with 0–3 years lag [32]. Their sustained population decline during past three decades was likely caused by sustained climate warming which is far from it optimum point for rodents. Climate warming approaching to optimum temperature would promote rodent abundance, however extreme warming would reduce rodent abundance. Using millennia data, Johnson, et al. found climate warming depressed population cycling of larch bud moth in Europe by altering their temperature from its optimal point. A few recent studies demonstrated that high temperatures could prohibit the recruitment and pregnancy rate of rodents in Inner Mongolia [35], reduce maturation rates and reproductive copulation attempts of rodents in South Africa [36], or alter the spatial distribution [37]. Temperature showed a dome-shaped effects on Apodemus agrarius with the optimum temperature of 21 °C in northern China [33], 22.1–25 °C in southern China [34]. Furthermore, we found more population showed decline in high-latitude regions of China. This is likely because the high-latitude region experienced more increase of temperature during past decades [38], which caused extra negative effects on population of rodents in these regions. In Europe, the population decline of lemmings was suggested to be affected by climate warming [17] which prevented forage under the snow coverage due to the hard ice layers caused by snow melting. A recent study revealed that climate warming was associated with the pole-ward contraction of southern boundary of Brandt’s voles, particularly when maximum air temperature is closely to the thermal neutral zone [41].

Sustained climate warming may disrupt the life-history of rodents if activity cycles do not match well with plant phenology. For example, Inouye et al. found under continued warming, the hibernating yellow-bellied marmots (Marmota flaviventris) appeared above ground 38 days earlier than observed 23 years ago [42]. Climate warming could pose stronger effects on intensified human influence habitats (such as cities and farmland), accelerate the decline of rodent population, because the habitat fragmentation caused by human activates could prevent the movement of animals under climate change [43].

Apart from direct effects, temperatures may have indirect effects which are opposite to that of direct effects. For example, Tian et al. found droughts boosted outbreaks of locusts in ancient China during AD 1–1900, but climate warming depressed its outbreaks by reducing drought frequency [44]. Jiang et al. found that temperature not only had a direct positive effect on rodent abundance in Inner Mongolia, but also had a negative indirect effect via altering the vegetation coverage on some rodent species, because many rodents in the grasslands do not like high or dense grass because they are less alerted to potential attacks by predators and their social communications is more difficult [45].

Effects of precipitation and density-dependency

Precipitation is generally thought to promote population growth of small rodents by increasing food resources or shelters [46, 47]. Previous studies found that precipitation was highly associated with rodent populations in both desert and tropical ecosystems [48]. Precipitation could facilitate rodent abundance of Peromyscus leucopus in North America, and for rodents in Arizona, via the increase of food resources both as vegetation biomass and seed production [49]. The positive association between precipitation and rodent abundance was found in southern and eastern Australia [50], western South America [51], Africa [52] and Inner Mongolia, China [53]. In this study, we found precipitation showed positive (current or with 1-yr time lag) associations with pooled population and four rodent species, including Apodemus agrarius, Mus musculus, Spermophilus dauricus, and Cricetulus barabensis, which is consistent with some previous studies, For example, cold and high precipitation increased the capture rate of Apodemus flavicollis in Poland [54], and high precipitation promoted population abundance of Mus musculus and Spermophilus dauricus by increased vegetation coverage and food production in Australia and northern China [55, 56].

Density-dependency is well recognized as a major regulator of population dynamics [57, 58]. Density dependence effects were reported in populations of hares and lynx in the boreal forests of North America and Scandinavia [6, 59], multimammate rat in fallow land of Tanzania [52], ungulates in The Rocky Mountains of the USA [60], and Brandt’s voles in the grasslands of China [61]. In this study, we found most rodent species showed positive density-dependency effects from their previous abundance to the population abundance in the following year. For those showing non-significant density-dependency effects (i.e. Rhombomys opimus, Niviventer confucianus, Apodemus peninsulae, and Phodopus sungorus), there is only one long-term time series available for those species.

Conclusions and implications

Rodents have been listed as major pests to humans. The good news is that the observed sustained population declines for 54 out of 115 rodent populations due to climate warming or human disturbance could benefit pest management programs in some rodent-infested regions or ecosystems. It is predicted that with on-going accelerated climate warming and human disturbance, the rodent damage problems will be alleviated in the observed regions. However, climate warming would increase rodent problems at higher elevations or higher latitudes due to their invasions into these regions. Because climate warming would bring more precipitation in arid regions of western China [38], it may also increase rodent abundance and lead to a higher prevalence of rodent-borne diseases such as Hemorrhagic Fever with Renal Syndrome (HFRS) in these regions [62], climate proxies could be useful for the forecast and control of rodent-associated zoonosis [63].

The bad news is that the large-scale of population declines of rodents may impose risk to natural ecosystems. Under the successive climate warming in the past decades, the period from 1983 to 2012 (almost overlap with the study period) was likely the warmest 30-year period of the last 1400 years in the Northern Hemisphere [64]. A previous study indicated that global warming negatively affected abundance of lynx (Lynx canadensis) population and damped the 10-year cycles [6]. The natural cycles of rodents are 3–5 or 9–11 years, the observed sustained population declines for over 2 or 3 decades suggest that the natural cycles of rodents are disrupted by the accelerated climate warming and intensified human disturbance. Rodents are relatively more resistant to environmental disturbance and make up a large proportion of mammalian fauna, thus, the collapse of the rodent community may be a warning signal of ecosystem transition or dysfunction. Rodents are important consumers, prey of many predator species, and seed dispersers for trees [65]. They play an important role in maintaining ecosystem function as keystone species in many ecosystems [66]. Collapse of rodent communities will significantly disrupt ecosystem function and ecosystem services. Therefore, it is necessary to conserve some beneficial or rare species while managing the rodent pest species.

Materials and methods

Rodent abundance data

We obtained rodent abundance data from the published scientific literature and survey reports since 1963, covering 20 provinces in China. Each datum consisted of following information: reference, species name, location (province, prefecture, and county), habitat type, abundance type (population density, capture rate, etc.), year, and abundance value. We only used data since 1980 to test the effects of climate warming and human disturbance (as represented by Gross Domestic Production, GDP) on change of population abundance of small rodents because successive rodent abundance data are rare before 1980. After 1980, the earth experienced rapid climate warming, and China experienced rapid economic growth. Analysis focusing on this period would help to identify their distinct effects on rodents.

Rodent abundance data was verified by using conservative data, for example, we removed data with uncertain species (i.e. unclear species names), time resolution larger than one year, location resolution larger than prefecture level. To detect the trend and changes of rodent population abundance at each location, we only used time series of commonly-seen (presence over half years of over the study period) species with the survey period equal or larger than 20 years and the data was surveyed by the same research team. There are four types of rodent abundance in the literature and survey reports: trap success (i.e. capture rate %, making up 79% of the data), population density (numbers of rodents per hectare, making up 20% of the data), and relative population density (active holes per hectare, making up 1% of the data). There are six habitat types: forest, grassland, farmland, grassland—farmland mosaic, city, and mixed habitats. We assigned latitude and longitude coordinates of geographical locations to the capital location of the prefecture using Amap (lbs.amap.com). Population abundance of multiple locations in a given county was calculated by the average of all locations in the same habitat. Yearly population abundance was calculated by the average of all monthly or seasonal data for a given year. After data verification, a total of 115 time series (80 populations from 18 known rodent species, 35 mixed populations from unknown species) was used in this study for further analysis.

Anthropogenic and climate proxy data

China experienced rapid economic growth as reflected by the gross domestic product (GDP) since the economic reform and opening up in 1978, accompanying with a high-speed of industrialization and urbanization. GDP is closely correlated with urban land expansion [67]. Therefore, GDP in each prefecture was used to represent human impact during 1980–2016. The GDP data was collected from national, provincial, and local statistical yearbooks or work reports compiled by statistical bureau or government. A small proportion of GDP data (4.5%) data was not available in the early 1980s for a few prefectures (making up 17.9%). We calculated these GDP based on the GDP of the next year and the average of the provincial GDP growth rate.

We used annual mean air temperature and annual precipitation from Chinese surface meteorological stations as climate proxy. Those data were obtained from the dataset of monthly surface observation (http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_MON.html), which was derived from monthly reports by each provincial meteorological department. The annual mean air temperature was calculated by the average of monthly air temperature, and the annual precipitation was calculated by the sum of monthly precipitation, a small proportion of temperature data (0.16%) data was not available in a few months, we assigned the temperature based on 50-yr mean temperature.

Statistical analysis

Generalized additive models (GAM) were used to model the effects of human disturbance (annual GDP) and climate change (annual mean air temperature and annual precipitation) on population abundance of each species (or mixtures of populations with several unknown species) [68]. Time series of rodent abundance and environmental variables were normalized by (x-min)/range to remove the impact of spatial differences of environmental variables [69]. Density-dependency and 1-yr time delayed effect of population abundance of rodents and climate were included in the modeling analysis. Variables with strong and significant correlation (r < − 0.6 or r > 0.6; p < 0.05) with the other variables were removed to avoid collinearity effects (Additional file 1: Fig. S4). To model the effects of human disturbance and climate change on rodent abundance of each rodent species, a global Gaussian GAMs of the population abundance \({\mathrm{Y}}_{\mathrm{t}}\)(\({\mathrm{Y}}_{\mathrm{t}}\)) for each species against the density dependence, human disturbance, climate (temperature, and precipitation) was fitted by using the formula:

$${Y}_{t}={a}_{t}+{b}_{t-1}{D}_{t-1}+{c}_{t}{A}_{t}+{d}_{t}{T}_{t}+{e}_{t-1}{T}_{t-1}+{f}_{t}{P}_{t}+{g}_{t-1}{P}_{t-1}+H+s\left(Lon, Lat\right){+\varepsilon }_{t}$$
(1)

where, \({Y}_{t}\) was the normalized population abundance at time t, \({D}_{t-1}\) was the population abundance of last year, \({A}_{t}\) was the anthropogenic effects (i.e. GDP),\({T}_{t}\) was the temperature, \({P}_{t}\) was the precipitation, \(H\) was the habitat,\(s\left(Lon, Lat\right)\) was a 2D smooth function (with k value, dimension of the basis = 4) for modeling the spatial autocorrelation effects [70]; \({\varepsilon }_{t}\) was uncorrelated random errors of zero mean and finite variance. \({a}_{t}\), \({b}_{t-1}\), \({c}_{t}\),\(d\), \({e}_{t}\), \({f}_{t}\), and \({g}_{t}\) were constants (\({a}_{t}\) was an intercept, \({b}_{t-1}\) and \({c}_{t}\) represented density dependent and anthropogenic (GDP) effects, \({d}_{t}\) and \({f}_{t}\), represented effects of temperature and precipitation, \({e}_{t-1}\) and \({g}_{t-1}\) represented one year delayed effects of temperature and precipitation on the population abundance \({Y}_{t}\) of species (or group)). For each species (or group), habitat and spatial autocorrelation were not considered if there were data with less than 5 prefectures. \({D}_{t-1}\) represents the density dependency which would help to minimize the temporal autocorrelation of time series.

A sub-model set of all possible sub-models was generated from the global model, the model selection criterion is AICC (Akaike Information Criterion correction for small sample size, which is commonly used and suitable for modelling linear responses, Additional file 1: Table S6) of each sub-model [71]. The process of GAM model analysis was shown in a schematic diagram (Additional file 1: Fig. S5).

To model the effects of body mass of rodents, habitat type, and latitudinal of a local population on the changing trend of rodent abundance, and their effects on association coefficients between population abundance and variables of human disturbance and climate change in Eq. 1, a Gaussian GAMs was fitted by using the formula:

$$Y=a+bB+cLat{+H+\varepsilon }_{t}$$
(2)

where, \(Y\) was the changing trend (1 denotes increase, − 1 for decrease, and 0 for no trend) of rodent abundance, or association coefficients (i.e. \({c}_{t}\), \({d}_{t}\), \({e}_{t-1}\), \({f}_{t}\), and \({g}_{t-1}\) in Eq. 1 after model selection). \(B\) was the body mass (g, log-transformed) of rodent species, \(Lat\) was the latitude of a local population, \(H\) was the habitat type; \({\varepsilon }_{t}\) was uncorrelated random errors of zero mean and finite variance. \(a\), \(b\), and \(c\) were constants (\(a\) was an intercept, \(b\) and \(c\) represented the effects of body mass of rodent and latitude of the local population).

We used linear regression to detect the changing trend of population abundance of each known rodent species or from mixed populations of unknown species. We quantified temporal variation in each time series by a kernel-smoothed estimate of the 10%, 50%, and 90% quantile of the normalized data sliding along the time series, which showed the amplitude of abundance valley (≥ 10%), hillside (≥ 50%), and peak (≥ 90%). We used wavelet analysis to identify nonstationary periodic characteristics of the frequency and amplitude of detrended population oscillations vary through time [72]. The wavelet power spectrum represents the integration of population oscillation strength over the influence period (time scale) and the series spans [73].

Linear regression was carried out via stats library in R (v.4.1.3) [74]. Kernel-smoothed quantiles estimation was carried out via KernSmooth library (v. 2.23-15) in R (v. 4.1.3) [74]. Generalized Additive Models (GAM) were carried out using the mgcv library (v. 1.8-15) [75] and MuMIn library (v. 1.43.6) [76] in R (v. 4.1.3). Wavelet analysis and time series detrending were carried out via biwavelet (v.0.20.19) and mFilter (v.0.1-5) library in R (v. 4.1.3) [77, 78].

Availability of data and materials

Data used in this study was provided in Additional file 2: Data S1.

References

  1. Hughes L. Biological consequences of global warming: is the signal already apparent? Trends Ecol Evol. 2000;15(2):56–61.

    CAS  PubMed  Article  Google Scholar 

  2. IPCC: Climate Change 2022: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. In. Cambridge 2022.

  3. Dirzo R, Young HS, Galetti M, Ceballos G, Isaac NJB, Collen B. Defaunation in the anthropocene. Science. 2014;345:401–6.

    CAS  PubMed  Article  Google Scholar 

  4. Chen IC, Hill JK, Ohlemuller R, Roy DB, Thomas CD. Rapid range shifts of species associated with high levels of climate warming. Science. 2011;333(6045):1024–6.

    CAS  PubMed  Article  Google Scholar 

  5. Román-Palacios C, Wiens JJ. Recent responses to climate change reveal the drivers of species extinction and survival. Proc Natl Acad Sci. 2020;117(8):4211–7.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. Yan C, Stenseth NC, Krebs CJ, Zhang Z. Linking climate change to population cycles of hares and lynx. Glob Change Biol. 2013;19(11):3263–71.

    Google Scholar 

  7. Xu L, Schmid BV, Liu J, Si X, Stenseth NC, Zhang Z. The trophic responses of two different rodent-vector-plague systems to climate change. Proc R Soc B-Biol Sci. 1800;2015:282.

    Google Scholar 

  8. Kaarlejarvi E, Eskelinen A, Olofsson J. Herbivores rescue diversity in warming tundra by modulating trait-dependent species losses and gains. Nat Commun. 2017;8:8.

    Article  CAS  Google Scholar 

  9. Hanski I, Henttonen H, Korpimaki E, Oksanen L, Turchin P. Small-rodent dynamics and predation. Ecology. 2001;82(6):1505–20.

    Article  Google Scholar 

  10. Vander Wall SB. Food hoarding in animals. Chicago: University of Chicago Press; 1990.

    Google Scholar 

  11. Stenseth NC. Population cycles in voles and lemmings: density dependence and phase dependence in a stochastic world. Oikos. 1999;87(3):427–61.

    Article  Google Scholar 

  12. Fauteux D, Stien A, Yoccoz Nigel G, Fuglei E, Ims Rolf A. Climate variability and density-dependent population dynamics: Lessons from a simple High Arctic ecosystem. Proc Natl Acad Sci. 2021;118(37): e2106635118.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Cornulier T, Yoccoz NG, Bretagnolle V, Brommer JE, Butet A, Ecke F, Elston DA, Framstad E, Henttonen H, Hornfeldt B, et al. Europe-wide dampening of population cycles in keystone herbivores. Science. 2013;340(6128):63–6.

    CAS  PubMed  Article  Google Scholar 

  14. Korpela K, Helle P, Henttonen H, Korpimäki E, Koskela E, Ovaskainen O, Pietiäinen H, Sundell J, Valkama J, Huitu O. Predator-vole interactions in Northern Europe: the role of small mustelids revised. Proc Biol Sci. 2014;281(1797):20142119.

    PubMed  PubMed Central  Google Scholar 

  15. Gouveia A, Bejček V, Flousek J, Sedláček F, Šťastný K, Zima J, Yoccoz NG, Stenseth NC, Tkadlec E. Long-term pattern of population dynamics in the field vole from central Europe: cyclic pattern with amplitude dampening. Popul Ecol. 2015;57(4):581–9.

    Article  Google Scholar 

  16. Tchabovsky A, Savinetskaya L, Surkova E. Breeding versus survival: proximate causes of abrupt population decline under environmental change in a desert rodent, the midday gerbil (Meriones meridianus Pallas, 1773). Integr Zool. 2018;14(4):366–75.

    Article  Google Scholar 

  17. Kausrud KL, Mysterud A, Steen H, Vik JO, Østbye E, Cazelles B, Framstad E, Eikeset AM, Mysterud I, Solhøy T, et al. Linking climate change to lemming cycles. Nature. 2008;456:93.

    CAS  PubMed  Article  Google Scholar 

  18. Kletty F, Tissier M, Kourkgy C, Capber F, Zahariev A, Chatelain N, Courtecuisse J, Le Maho Y, Habold C. A focus on the European hamster to illustrate how to monitor endangered species. Integr Zool. 2019;14(1):65–74.

    PubMed  Article  Google Scholar 

  19. Brommer JE, Pietiäinen H, Ahola K, Karell P, Karstinen T, Kolunen H. The return of the vole cycle in southern Finland refutes the generality of the loss of cycles through ‘climatic forcing.’ Glob Change Biol. 2010;16(2):577–86.

    Article  Google Scholar 

  20. Pech RP, Hood GM, Singleton GR, Salmon E, Forrester RI, Brown PR. Models for predicting plagues of house mice (Mus musculus) in Australia. In: Ecologically-Based Management of Rodent Pests. Edited by Singleton GH, L; Leirs, H; Zhang, Z Canberra; 1999.

  21. Wang M, Zhong W, Wan X, Wang G. Habitat selection during dispersion of daurian pika (Ochotona daurica). Acta Zoologica Sinica. 1998;44(4):398–405.

    Google Scholar 

  22. Fan N, Zhou W, Wei W, Wang Q, Jiang Y. Rodent pest management in the Qinghai-Tibet alpine meadow ecosystem. In: Ecologicaly-based rodent management. Edited by Singleton GR, Hinds LA, Leirs H, Zhang Z. Canberra: Australian Centre for International Agricultural Research 1999.

  23. Zhang ZB, Pech R, Davis S, Shi DZ, Wan XR, Zhong WQ. Extrinsic and intrinsic factors determine the eruptive dynamics of Brandt’s voles Microtus brandti in Inner Mongolia, China. Oikos. 2003;100(2):299–310.

    Article  Google Scholar 

  24. Li G, Hou X, Wan X, Zhang Z. Sheep grazing causes shift in sex ratio and cohort structure of Brandt’s vole: implication of their adaptation to food shortage. Integr Zool. 2016;11(1):76–84.

    CAS  PubMed  Article  Google Scholar 

  25. Yan C, Xu L, Xu TQ, Cao XP, Wang FS, Wang SQ, Hao SS, Yang HF, Zhang ZB. Agricultural irrigation mediates climatic effects and density dependence in population dynamics of Chinese striped hamster in North China Plain. J Anim Ecol. 2013;82(2):334–44.

    PubMed  Article  Google Scholar 

  26. Cooper EJ. Warmer shorter winters disrupt arctic terrestrial ecosystems. Annu Rev Ecol Evol Syst. 2014;45(1):271–95.

    Article  Google Scholar 

  27. Renner SS, Zohner CM. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu Rev Ecol Evol Syst. 2018;49(1):165–82.

    Article  Google Scholar 

  28. Krebs CJ, Boonstra R, Gilbert BS, Kenney AJ, Boutin S. Impact of climate change on the small mammal community of the Yukon boreal forest. Integr Zool. 2019;14(6):528–41.

    PubMed  PubMed Central  Article  Google Scholar 

  29. Zdzisław P, Wlodzimierz J, Bogumiła J, Michalina P. Rodent population-dynamics in a primeval deciduous forest (Bialowieza national park) in relation to weather, seed crop, and predation. Acta Theriol. 1993;38(2):199–232.

    Google Scholar 

  30. Fugui Q, Weicheng F, Xueli B, Xinghu W, Liangjun Z. Analysis of plague monitoring of Xiji county from 1986 to 2005. Endemic Dis Bull. 2007;22(6):19–21.

    Google Scholar 

  31. Wanhong D, Xingming H. Analysis of plague surveillance results in Haiyuan County, Ningxia during 1981–2010. J Med Pest Control. 2011;27(6):545–6.

    Google Scholar 

  32. Dingsheng W, Pengfei G, Yongqiang S, Jinxiao X, Daqin X. The correlation of meteorological factors with the numbers of marmots and parasitic fleas in the marmot plague foci in Subei County and Sunan County of Gansu Province. Chin J Endemiol. 2019;38(11):873–7.

    Google Scholar 

  33. Xiang H, Ning H, Tuo F, Bo Z, Ning CX, Jing W, Gang C. Nonlinear effects of climate on population dynamics of Apodemus agrarius. Acta Ecol Sin. 2020;40(14):4836–41.

    Google Scholar 

  34. Wenshu Y, Zaixue Y. Effects of different meteorological factors on Apodemus agrarius population in farmland in Xifeng County, Guizhou Province, China. Plant Prot. 2020;40(8):35–40.

    Google Scholar 

  35. Liu W, Wan X, Zhong W. Population dynamics of the Mongolian gerbils: seasonal patterns and interactions among density, reproduction and climate. J Arid Environ. 2007;68(3):383–97.

    Article  Google Scholar 

  36. Nater CR, Canale CI, van Benthem KJ, Yuen CH, Schoepf I, Pillay N, Ozgul A, Schradin C. Interactive effects of exogenous and endogenous factors on demographic rates of an African rodent. Oikos. 2016;125(12):1838–48.

    Article  Google Scholar 

  37. Guo S, Li G, Liu J, Wang J, Lu L, Liu Q-Y. Dispersal route of the Asian house rat (Rattus tanezumi) on mainland China: insights from microsatellite and mitochondrial DNA. BMC Genet. 2019;20:11.

    PubMed  PubMed Central  Article  Google Scholar 

  38. He J, Yan C, Holyoak M, Wan X, Ren G, Hou Y, Xie Y, Zhang Z. Quantifying the effects of climate and anthropogenic change on regional species loss in China. PLoS ONE. 2018;13(7):e0199735.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. Yabe T, Minato R, Hashimoto T. Breeding under snow cover in Norway rats (Rattus norvegicus) on uninhabited islands in Hokkaido, Japan. Russ J Theriol. 2017;16:43–6.

    Article  Google Scholar 

  40. Ylonen H, Haapakoski M, Sievert T, Sundell J. Voles and weasels in the boreal Fennoscandian small mammal community: what happens if the least weasel disappears due to climate change? Integr Zool. 2019;14(4):327–40.

    PubMed  PubMed Central  Article  Google Scholar 

  41. Bai D, Wan X, Li G, Wan X, Guo Y, Shi D, Zhang Z. Factors influencing range contraction of a rodent herbivore in a steppe grassland over the past decades. Ecol Evol. 2022;12(2): e8546.

    PubMed  PubMed Central  Article  Google Scholar 

  42. Inouye DW, Barr B, Armitage KB, Inouye BD. Climate change is affecting altitudinal migrants and hibernating species. Proc Natl Acad Sci. 2000;97(4):1630–3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Li X, Jiang G, Tian H, Xu L, Yan C, Wang Z, Wei F, Zhang Z. Human impact and climate cooling caused range contraction of large mammals in China over the past two millennia. Ecography. 2015;38(1):74–82.

    Article  Google Scholar 

  44. Tian H, Stige LC, Cazelles B, Kausrud KL, Svarverud R, Stenseth NC, Zhang Z. Reconstruction of a 1,910-y-long locust series reveals consistent associations with climate fluctuations in China. Proc Natl Acad Sci. 2011;108(35):14521–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Jiang G, Zhao T, Liu J, Xu L, Yu G, He H, Krebs CJ, Zhang Z. Effects of ENSO-linked clilmate and vegetation on population dynamics of sympatric rodent species in semiarid grasslands of Inner Mongolia, China. Can J Zool. 2011;89(8):678–91.

    Article  Google Scholar 

  46. Lima M, Keymer JE, Jaksic FM. El Niño-Southern Oscillation–driven rainfall variability and delayed density dependence cause rodent outbreaks in western South America: linking demography and population dynamics. Am Nat. 1999;153(5):476–91.

    PubMed  Article  Google Scholar 

  47. Lima M, Marquet PA, Jaksic FM. El Niño events, precipitation patterns, and rodent outbreaks are statistically associated in semiarid Chile. Ecography. 1999;22(2):213–8.

    Article  Google Scholar 

  48. Ernest SKM, Brown JH, Parmenter RR. Rodents, plants, and precipitation: spatial and temporal dynamics of consumers and resources. Oikos. 2000;88(3):470–82.

    Article  Google Scholar 

  49. Brown JH, Ernest SKM. Rain and rodents: complex dynamics of desert consumers: although water is the primary limiting resource in desert ecosystems, the relationship between rodent population dynamics and precipitation is complex and nonlinear. Bioscience. 2002;52(11):979–87.

    Article  Google Scholar 

  50. Roger P, Greg H, Grant S, Elizabeth S, Robert F, Peter B. Models for predicting plagues of house mice (Mus domesticus) in Australia. In: Ecologically-Based Management of Rodent Pests. ACIAR monographs series; 1999.

  51. Jaksic FM, Silva SI, Meserve PL, Gutierrez JR. A long-term study of vertebrate predator responses to an El Nino (ENSO) disturbance in western South America. Oikos. 1997;78(2):341–54.

    Article  Google Scholar 

  52. Leirs H, Stenseth NC, Nichols JD, Hines JE, Verhagen R, Verheyen W. Stochastic seasonality and nonlinear density-dependent factors regulate population size in an African rodent. Nature. 1997;389(6647):176–80.

    CAS  PubMed  Article  Google Scholar 

  53. Li Z, Zhang W. Analysis on the relation between population of Meriones unguiculatus and factors of meterological phenomena. Acta Therologica Sinica. 1993;13:131–5.

    Google Scholar 

  54. Wróbel A, Bogdziewicz M. It is raining mice and voles: which weather conditions influence the activity of Apodemus flavicollis and Myodes glareolus? Eur J Wildl Res. 2015;61(3):475–8.

    Article  Google Scholar 

  55. Zhang Z, Wang Z. Ecology and management of rodent pests in agricluture (in Chinese). Beijing: China ocean press; 1998.

    Google Scholar 

  56. White T. Outbreaks of house mice in Australia: Limitation by a key resource. Crop Pasture Sci. 2002;53:505–9.

    Article  Google Scholar 

  57. Turchin P. Rarity of density dependence or population regulation with lags? Nature. 1990;344(6267):660–3.

    Article  Google Scholar 

  58. Krebs CJ. Review of the chitty hypothesis of population regulation. Can J Zool. 1978;56(12):2463–80.

    Article  Google Scholar 

  59. Stenseth NC, Falck W, Bjornstad ON, Krebs CJ. Population regulation in snowshoe hare and Canadian lynx: asymmetric food web configurations between hare and lynx. Proc Natl Acad Sci USA. 1997;94(10):5147–52.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. Wang G, Hobbs NT, Boone RB, Illius AW, Gordon IJ, Gross JE, Hamlin KL. Spatial and temporal variability modify density dependence in populations of large herbivores. Ecology. 2006;87(1):95–102.

    PubMed  Article  Google Scholar 

  61. Yin BF, Li GL, Wan XR, Shang GZ, Wei WH, Zhang ZB. Large manipulative experiments reveal complex effects of food supplementation on population dynamics of Brandt’s voles. Sci China-Life Sci. 2017;60(8):911–20.

    CAS  PubMed  Article  Google Scholar 

  62. Wu G, Xia Z, Wang F, Wu J, Cheng D, Chen X, Liu H, Du Z. Investigation on risk factors of haemorrhagic fever with renal syndrome (HFRS) in Xuancheng City in Anhui Province, Mainland China. Epidemiol Infect. 2020;148:e248.

    PubMed  PubMed Central  Article  Google Scholar 

  63. Xu L, Stige LC, Leirs H, Neerinckx S, Gage KL, Yang R, Liu Q, Bramanti B, Dean KR, Tang H, et al. Historical and genomic data reveal the influencing factors on global transmission velocity of plague during the Third Pandemic. Proc Natl Acad Sci. 2019;116(24):11833–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva: IPCC; 2014.

  65. Yang X, Yan C, Zhao Q, Holyoak M, Fortuna MA, Bascompte J, Jansen PA, Zhang Z. Ecological succession drives the structural change of seed-rodent interaction networks in fragmented forests. For Ecol Manage. 2018;419–420:42–50.

    Article  Google Scholar 

  66. Stapp P. Long-term studies of small mammal communities in arid and semiarid environments. J Mammal. 2010;91(4):773–5.

    Article  Google Scholar 

  67. Seto KC, Fragkias M, Guneralp B, Reilly MK. A meta-analysis of global urban land expansion. PLoS ONE. 2011;6(8):9.

    Google Scholar 

  68. Wood SN. Generalized additive models: an introduction with R (2nd edition). Boca Raton: Chapman and Hall/CRC; 2017.

    Book  Google Scholar 

  69. Milligan GW, Cooper MC. A study of standardization of variables in cluster analysis. J Classif. 1988;5(2):181–204.

    Article  Google Scholar 

  70. Trevor H, Robert T. Generalized additive models. London: Chapman and Hall; 1990.

    Google Scholar 

  71. Grueber CE, Nakagawa S, Laws RJ, Jamieson IG. Multimodel inference in ecology and evolution: challenges and solutions (vol 24, pg 699, 2011). J Evol Biol. 2011;24(7):1627–1627.

    Article  Google Scholar 

  72. Torrence C, Compo GP. A practical guide to wavelet analysis. Bull Am Meteor Soc. 1998;79(1):61–78.

    Article  Google Scholar 

  73. Liu Y, Liang XS, Weisberg R. Rectification of the bias in the wavelet power spectrum. J Atmos Oceanic Tech. 2007;24:2093–102.

    Article  Google Scholar 

  74. R Core Team: R: a language and environment for statistical computing. In: R Foundation for Statistical Computing. 2022.

  75. Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J Royal Stat Soc Ser B Stat Methodol. 2011;73:3–36.

    Article  Google Scholar 

  76. Bartoń K: MuMIn: Multi-Model Inference. In.; 2018.

  77. Gouhier TC, Grinsted A, Simko V: R package {biwavelet}: Conduct Univariate and Bivariate Wavelet Analyses. In., (Version 0.20.19) edn; 2019.

  78. Balcilar M: mFilter: Miscellaneous Time Series Filters. In., R package version 0.1–5 edn; 2019.

Download references

Acknowledgements

We thank Weiling Chai, Shaowei Liang, and the other students from Jiangxi Normal University for excellent data editing assistance.

Funding

This work was supported by the Major Program of the National Natural Science Foundation of China (Grant 32090021), ANSO Project of Chinese Academy of Science (Grant ANSO-CR-KP-2020-08), a grant from Ministry of Science and Technology (2019FY100305), the Young Scientists Fund of the National Natural Science Foundation of China (Grant 32101259), and the Young Elite Scientists Sponsorship Program by CAST and ISZS (2021QNRC001, ISZS-YESS Program).

Author information

Authors and Affiliations

Authors

Contributions

ZZ designed the study. XW and ZW contributed to data compilation and editing. XW and CY contributed to data analysis. ZZ, XW, and CY contributed to paper writing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zhibin Zhang.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Tables S1 to S6. Figure S1 to S5.

Additional file 2: Data S1.

Data used in this study.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wan, X., Yan, C., Wang, Z. et al. Sustained population decline of rodents is linked to accelerated climate warming and human disturbance. BMC Ecol Evo 22, 102 (2022). https://doi.org/10.1186/s12862-022-02056-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12862-022-02056-z

Keywords

  • Rodent
  • Climate change
  • Human disturbance
  • Population dynamics
  • Ecosystem transition or dysfunction