Avian predation has the strongest impact on vole survival during winter and spring in temperate grasslands

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Bartoń This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7099831/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Predation is widely acknowledged as an important factor affecting small rodent populations, yet its specific impact on their dynamics is not fully understood. In seasonal environments, winter and early spring are critical periods for small mammal populations, as environmental stressors – including predation – coincide with an inability to offset mortality through reproduction. Although birds of prey are major rodent predators, their effect on prey populations during this period remains poorly quantified. To address this, we conducted a year-round field experiment in temperate grasslands, excluding avian predators from root vole ( Microtus oeconomus ) populations using net-covered plots in three locations. Vole survival and population size were assessed using capture–mark–recapture method, considering effects of sex and body mass of individuals. Our results show that avian predation significantly reduced vole survival during winter and spring (November–May), increasing mortality by up to 22%, even when under snow cover. In contrast, no effect was detected during the rest of the year, and bird predation did not influence seasonal population dynamics. Overwinter survival was negatively associated with body mass, with larger individuals experiencing higher mortality; this pattern was not modified by predation exclusion. These findings demonstrate that avian predators exert substantial seasonal pressure on vole survival, contributing to winter–spring population declines. However, the influence of bird predation appears limited in shaping long-term population dynamics. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Zoology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Predation pressure in seasonal environments can vary widely throughout the year, with winter being the critical period for both prey and predator populations. A prime example of prey in a multi-predator environment are small rodents, facing many natural enemies of widely varying sizes and hunting strategies. Unsurprisingly, predation has long been considered a factor responsible for shaping the dynamics of small rodent abundance (e.g. [ 1 – 5 ]). Small rodents often exhibit regular, periodic fluctuations in density, known as multi-year population cycles, characterised by a lagged density dependence on population growth rate (e.g. [ 6 ]). Despite extensive research, the role of predators in small rodent populations remains uncertain, including their potential contribution to generating multiannual cycles of abundance [ 7 , 8 ]. It has been posited that while predation may deepen and prolong the low phases of these cycles (e.g. [ 9 ]), predation alone is neither essential nor sufficient to generate a cyclic dynamics in a prey population [ 10 ]. Birds of prey are assumed to exert a strong impact on small mammals, capable of either suppressing or amplifying cyclicity of their prey populations, and to be an important source of density-dependent mortality in vole populations [ 11 , 12 ]. Studies involving radio-tracked voles have shown that raptors can achieve predation rates of approximately 30% during summer on experimental root vole ( Microtus oeconomus ) populations in Norway [ 11 ] and on natural field vole populations in Finland [ 13 ]. Avian predation intensity is influenced by the availability of prey (phase of the cycle) and the environmental structure affecting visibility and hunting efficiency for birds [ 11 , 14 ][Steen 1994, Ims and Andreassen 2000]. The risk of rodent predation by birds has been found to increase with environmental openness [ 15 ], with raptors preferring environments with low vegetation and high densities of voles, as this facilitates hunting [ 16 , 17 ]. Although the effect of mammalian sedentary specialist predators (small mustelids) on vole demography has been widely documented [ 12 , 18 ], studies analysing the role of avian predation are scarce. In contrast to small mustelids ( Mustela nivalis and M. erminea ), which have often been indicated as a possible factor causing population cycles in northern populations (e.g., [ 19 – 22 ]), birds of prey, particularly migratory species like kestrel ( Falco tinnunculus tinnunculus ), tend to display the fastest quantitative responses to changes in prey availability. Consequently, they may stabilise prey population dynamics rather than induce abundance cycles, often promoting spatial synchronisation [ 11 , 23 – 25 ]. Some evidence suggests that, in certain populations, the predation pressure exerted by these birds in spring and early summer may complement the pressure from weasels, requiring a longer time lag to produce an abundance cycle [ 26 ]. Most research on the effect of predation has focused on multiannual dynamics (cycles) of small rodent populations, but the role of avian predators in shaping the seasonal dynamics remains unresolved. It is often assumed that raptors do not affect vole demographics during winter. The prevailing view is that vole populations in winter are more influenced by within-population factors, weather [ 27 , 28 ], or food availability [ 29 ]. Nevertheless, the impact of birds of prey on small mammal populations in autumn and winter remains to be adequately studied. In northern ecosystems, where most studies on this topic have been done, this shortage of research on bird predation on small mammals may be due to the fact that the snow cover provides a natural concealment for potential prey [ 30 ]. In temperate zone Europe, however, the snow cover is typically thinner and does not persist throughout the whole winter. Studies on the dietary habits of avian predators have indicated that voles constitute a significant portion of their winter diet [ 31 , 32 ]. Furthermore, birds of prey may affect the population structure of small rodents through differential predation on different sex or age classes of prey [ 33 – 35 ]. To accurately assess the impact of predation, experimental approaches that either exclude or maintain predation pressure are essential. To date, only a few experiments have involved the exclusion of bird predation, and these have largely been limited to boreal ecosystems and enclosed populations [ 29 , 36 – 38 ]. Results from these experiments indicate that in the boreal zone, reducing predation by birds led to a significant increase in the field vole ( Microtus agrestis ) population size and reversed the typical summer decline in their population [ 36 ]. Similar results were obtained in enclosed populations of voles and lemmings in aviaries, where excluding predation by both birds and mammalian predators [ 29 , 37 , 38 ] altered abundance dynamics and reversed decline phases. In our study, we examine the impact of avian predation on free-living root voles in temperate grassland habitats. Drawing on results from previous studies showing that predation can limit population growth of small mammals, we hypothesise that predation by birds of prey would substantially affect vole survival. Therefore, we predict that by preventing avian predation, vole survival would increase during the winter and spring, leading to a rise in the population density of voles at the start of the breeding season. Studies suggest that male voles [ 39 ] and younger individuals [ 40 , 41 ] are generally more vulnerable to bird predation due to their higher locomotory activity and preference for open micro-habitats. Therefore, we considered that predation rate will be affected by sex and body mass of voles. To test these hypotheses, we conducted a spatially replicated field experiment involving exclusion of predation by birds, and monitoring vole populations throughout the year. Results In total, we recorded 3984 captures of 1731 individuals, of which 59.2% (n = 1025) were recaptured at least once (see Table S1 in the ESM). In each session and plot, a mean of 57.42 (± 15.1 s.d.) individuals were caught, with numbers ranging from 26 to 94 (see Table S2 in the ESM). An average individual was captured during 1.59 (± 0.98 s.d.) sessions, or on 2.3 (± 2.0 s.d.) occasions (days). A 63% of individuals (n = 1093) were recorded in a single session, of which 831 – only on one occasion (i.e. one day). The average period over which voles were seen (from first to last capture) was 48.73 (± 81.9 s.d.) days (with a maximum of 455 days). Nine individuals were recorded for the entire duration of the study (i.e. those from the first cohort who were also recaptured during the last trapping session), of which three in every session (see Table S3 in the ESM). Model selection The four best-ranked capture-mark-recapture models contributed a total of 99% of the model weight ( ω ) (1). They all included, in addition to parameters that were fixed to all models (for survival, S : location, time and body mass; for capture probability, p : location interacting with time), the treatment effect, interaction of location and time (for S ), time, body mass interacting with time, and location interacting with time within session (for p ). For survival, the treatment effect was included in interaction with either time or location. The time effect in these models was included either in a categorical form or as 3- or 4-degree spline function. Cohort and sex were only included in lower-ranked models. Therefore, model selection suggests that the experimental treatment was overall an important factor for survival, and both the survival and the effect of body mass on survival varied with time. We used AIC c for the above model ranking, since the bootstrap goodness-of-fit test showed no lack of fit of the models to our data ( p = 0.6, ĉ = 1.02), indicating no need for quasi-likelihood adjustment. Six models had effectively non-zero weights (taken to be ω > 0.001), and all the estimates reported below are averaged over these models. Survival For about the first half of the study period (November to May), mean survival in the treatment plots was higher than in the respective control plots (Fig. 2 ; Figure S1 in the ESM). Relative survival, defined as a ratio of survival in treatment plots to that in control plots, was highest during winter, from November to March, with survival in control plots lower by 15%-22% than in treatment plots (p ≤ 0.003). Overall, the temporal pattern of survival was similar between plots, but differed slightly in the timing and magnitude of peaks and troughs. Estimated survival increased between January and March (until May at Barwik site), followed by a decrease between May and July, and then another increase, which was highest at Barwik. A period of high survival coincided with a low average body mass, and conversely, a decrease in survival occurred when captured individuals were heavier (Figure S2 in the ESM). Body mass The effect of the experimental treatment on body mass was intermittent and varied between sites (Figure S2 in the ESM). Overall, the proportion of heavier individuals (above ca. 30 g) decreased in winter, followed by an increase that started in spring (Table S4 in the ESM). After this increase, between May and July, the higher proportion of heavier individuals persisted for a shorter period in the treated plots than in the control plots. However, this effect was only significant in Barwik and to a lesser extent in Losiowka. Relationship between body mass and survival changed during the study period (Fig. 3 ). Survival decreased with body mass between November and May, therefore the largest individuals faced highest mortality. The average body mass of individuals captured over that period was 24.9 ± 4.6 g (range 16–44 g). In subsequent sessions (i.e. from May onwards) the models showed no effect of body mass on survival. This diminishing relationship between body mass and survival coincided with an increase in the proportion of larger individuals (in May, see Fig. 3 ; Figure S2 in the ESM), but as the share of larger individuals decreased again, the relationship remained flat. Population size The estimated population size varied substantially during the study, as well as between locations (see Fig. 4; Figure S3 in the ESM). However, there were common patterns, which included a peak before the onset of winter, a winter decline, and a subsequent increase that continued through the end of the study. Initial population sizes, prior to netting, differed within each pair of plots, with the control populations being less numerous in each location (on average in 74.5 (67–96) individuals in treatment, and 64.8 (43–82) in control plots, the mean percentage difference was 14%, see Table S5 in the ESM). Relatively largest difference was between plots at Losiowka (25 individuals or 43%). The observed increase in population size that started in spring was stronger in plots where avian predation was excluded. It was particularly apparent between March and May, when population size in all treatment plots was clearly larger that in the corresponding control plots. At Barwik, the faster population increase in the treatment plot had a lasting effect, with the population size remaining consistently larger than in the control plot until the study was concluded. In the other plots, exclusion of bird predation did not cause clear long-term effect in terms of population size. Discussion We investigated the impact of avian predation on vole survival and, in turn, on population dynamics, by experimentally excluding bird access to free-living root voles throughout an entire year (from November 2005 to November 2006). This allowed us to analyse the role of raptors that prey on voles during breeding and non-breeding periods, including winter. In line with our predictions, predation by avian predators exerted considerable pressure on voles, reducing their survival by up to 20%. We found that bird predation was limiting vole abundance in spring. In contrast, during the primary breeding season in summer, predator exclusion had little effect on vole survival in the studied populations. Thus, the obtained results partially supported our prediction that avian predators would be a significant factor in vole mortality during the breeding season, similar to what has been observed in vole populations in northern Europe [ 11 , 36 ]. The differences between our observations and earlier reports most likely stem from the fact that in our study area birds that prey on voles were present throughout the winter [ 42 ], which was not the case in the Norwegian [ 11 , 27 , 28 ] and Finnish [ 26 , 36 ] studies. The importance of the avian predation component in vole dynamics has been demonstrated by experimental manipulations in Finland [ 36 ], where reducing the number of both birds of prey and small mustelids reversed the decline in the density of Microtus voles, whereas this did not occur when only small mustelids’ population was reduced. Moreover, Norrdahl and Korpimäki [ 48 ] found that, together with the high vole mortality caused by birds during the breeding season, overall densities of small rodents increased during the summer in predator-reduced areas, while decreasing in control areas. A similar pattern was observed in experimental enclosures in Finland, where the exclusion of all major predators (both avian and mammalian) led to significant differences in vole abundance between the enclosure and control populations, suggesting that predation plays a crucial role in maintaining the low phase of the population cycle in voles [ 29 , 37 ]. We found that vole survival increased where raptors were excluded, mainly in winter and spring. Previous studies have given relatively little attention to the impact of winter avian predation on voles, probably due to the limited number of wintering raptor species and the presence of snow cover. Some studies have focused on the survival of voles during winter, but without distinguishing between avian and mammalian predation: in Norway [ 27 , 28 , 49 ], where vole-eating raptors were almost absent, in Finland [ 37 ], where all predators were excluded in experimental plots, in Sweden, where all predators except small mustelids were excluded by fencing and netting, and in Poland [ 50 ], where small mustelids were excluded by fencing. In contrast to the Norwegian studies [ 27 , 28 ], results from experiments with predator exclusion in Finland and Sweden [ 29 , 37 , 51 ] confirm that predation during the winter-spring period is an important factor in the overwintering survival of voles. Notably, there was a permanent snow cover in our study area from December to March (see Fig. 2 ), which may have effectively protected voles against bird predation [ 30 ]. However, among the birds of prey present in our study area in winter were two species of owls, the tawny owl and the long-eared owl [ 42 ]. Owls may use acoustic signals to hunt small mammals under the snow, which has been experimentally demonstrated [ 52 ]. Moreover, during our study, the snow cover in winter was relatively thin; the maximum thickness did not exceed 15 cm, which could facilitate owls in locating and hunting concealed prey. The ability of owls to hunt successfully even during snowy winters has also been indirectly confirmed by analysis of their winter diet composition, which is dominated by Arvicolidae rodents (e.g. [ 31 , 53 ]). In the present study, the survival rate of voles and their population dynamics were affected not only by experimental treatment but also showed considerable variation between the locations. Although the study plots were all situated in an open meadow, fine-scale local conditions may have influenced the predation and population dynamics of voles. For example, the structure and density of herbaceous vegetation, as well as the distance from the forest edge, can significantly impact bird predation [ 54 , 55 ]. The most pronounced impact of avian predation on vole survival and population dynamics was observed in a location (Barwik) situated closest to the forest edge (300 m), whereas the other two locations were approximately 900 m away from the edge of the forest. Numerous studies have shown that environmental factors—such as food availability, predators, shelters, competition, landscape structure, and micro-environmental variation—have a strong influence on the population dynamics of small mammals [ 29 , 56 – 58 ]. The role of habitat has been demonstrated by the results of the experiment on field voles in Kielder Forest, Scotland, where the environmental effects of homogeneous forest plantations influenced the phase of the abundance cycle in the population of this species [ 59 ]. Contrary to our expectations, we did not detect sex-related differences in the susceptibility of voles to bird predation. Previous studies have indicated that males are generally more vulnerable to bird predation due to their higher levels of motor activity [ 39 , 60 , 61 ]. However, we only considered the average effect of sex on survival throughout the year, i.e. without seasonal variation. Given that the study encompassed a period during which voles exhibited no sexual activity, and therefore when locomotor activity of both sexes was likely to be similar, this effect may have been small enough not to be detected. We found that the over-winter survival rate of voles decreased with their body mass. Although the relationship between the winter survival rates of small mammals and their body mass has been long recognised [ 62 , 63 ], it remains underexplored due to the limited number of studies focused on winter survival in general [ 28 , 50 , 64 , 65 ]. As older individuals tend to be heavier [ 42 , 50 ], lower survival may be directly related to age. Additionally, during seasonal food shortages, the higher food requirements of heavier individuals may lead to increased risk of starvation and reduce their survival rates [ 27 , 50 , 64 , 65 ]. Furthermore, in some species, including root voles, a seasonal decrease in body mass during winter has been documented, presumably related to a reduction in energy requirements [ 27 , 50 ]. Although the exclusion of avian predation did not alter the effect of body mass on survival, an inspection of the capture histories revealed it improved winter survival of voles who had reduced their body mass at the onset of winter (cf. “class #2” in Figure S4 in the ESM). The majority of individuals from this seasonal cohort were not recorded after the November session and only survived through the winter if avian predation was removed. One of the possible explanations is that heavier voles need to forage for longer, making them more vulnerable to predation. In winter, heavier voles may also be in worse condition, making them more vulnerable to predation by birds. It should also be noted that since the survival rate of larger voles is overall low, the effect of avian predation on larger voles was relatively stronger than on those with smaller body mass. Interestingly, we have observed a decrease in the survival rate of voles in both experimental and control plots, at the beginning of summer (from May to July). Therefore, this decline in survival cannot be explained by bird predation. This phenomenon may be analogous to what are known as “summer declines” in northern root vole populations, which are postulated to be caused by non-random prey selection by small mustelids [ 20 , 66 ]. However, tracing the fate of voles from different seasonal cohorts, each adopting a different life-history strategy for growth and maturation, reveals that this decrease in summer apparent survival primarily affects voles born in the autumn of the previous year, which mature and gain weight rapidly in spring (cf. “class #3” in Figure S4 in the ESM), suggesting that a major contributor to this decline is [indeed] mortality rather than emigration. Considering the seasonal population abundance dynamics, the impact of bird predation on root voles became apparent in early spring (March to May), when abundance increased briefly in plots with predation removed. This coincides with the arrival and breeding season of bird species that prey on voles [ 42 ]. In early spring, meadows provide the fewest shelters, making voles more vulnerable to attacks by birds of prey. Additionally, at the beginning of the breeding season, the open wet meadow in our study area is typically flooded, and vegetation is less dense, making it easier for predators to locate rodents. When vegetation becomes dense around July, it acts as a cover for voles from bird predators. This finding aligns with the results of earlier studies showing that the impact of birds of prey on the vole population is greatest in spring [ 51 , 67 ]. Winter conditions may be a key determinant of vole survival and, in consequence, their density at the onset of the breeding season, influencing both seasonal and multiannual population dynamics [ 27 , 29 , 68 ]. Erlinge et al. [ 69 ] showed that high winter predation can outweigh the summer reproduction of vole populations, stabilising population dynamics and dampening its cyclicity. The results of our study add another piece to the puzzle regarding the causes of vole population crashes in winter. Although previously thought to be mainly driven by climatic conditions [ 27 ], we demonstrate that part of the winter mortality of voles can be caused by avian predation, even during the persistence of snow cover. We also show that it is during the winter-spring period that avian predation has the greatest impact on vole survival and abundance. The changing climate will likely affect vole survival during the winter-spring period by altering the timing of when birds exert the highest predation pressure on voles, as it has already led to reduced rainfall and snow cover. This can make it easier for birds to prey on small mammals during winter, but may decrease their hunting success in early spring. Finally, it should be noted that avian predation can vary significantly depending on the phase of the population cycle, often being several times higher during the decline phase (e.g. [ 11 ]). Our study was carried out during the peak of the population [ 42 ], thus to fully understand the role birds of prey play in shaping vole population dynamics, further research is needed to examine this topic during the decline phase of the vole population. Material and methods Study area The study was conducted in the Lower Basin of the Biebrza National Park, NE Poland (53°36′18″N, 22°55′36″E). The study area is located in a homogeneous sedge wetland with the vegetation dominated by plants of the Cyperaceae family. The main plant species in the Park is the fibrous tussock sedge Carex appropinquata , which covers 85% of the area and forms hummock–hollow structures with tufts up to 1 m high (Fig. 1 ). In places, the sedge meadow is interspersed with shrubbery, including willows, birches and alders, indicative of an early stage of secondary succession. The wetland has a seasonal water regime with the highest level in spring when flooding is frequent. However, no spring flooding was recorded during the study period. The climate of the area combines continental and subboreal features, with long winters (> 100 days), a short and early spring, and a short growing season (77–85 days). The coldest month is February and the warmest is July, and the total annual precipitation is 550 mm. The winter (2005/2006) was characterised by permanent snow cover from December to the end of March. The meteorological data on the duration and thickness of the snow cover were obtained from the Biebrza National Park meteorological station. The community of diurnal birds of prey observed near the trapping grids included the common buzzard ( Buteo buteo ), rough-legged buzzard ( B. lagopus ), Western marsh harrier ( Circus aeruginosus ), hen harrier ( C. cyaneus ), and Montagu’s harrier ( C. pygargus ), as well as the lesser spotted eagle ( Clanga pomarina ). The highest numbers of diurnal vole-eating birds were recorded in spring (April-May) and autumn (November), and the lowest in early autumn (September) and winter (January-February) [ 42 ]. The area is also home to a resident year-round population of tawny owls ( Strix aluco ) and long-eared owls ( Asio otus ), as well as seasonally migrating short-eared owls ( Asio flammeus ). The main mammalian predators of the voles were the red fox ( Vulpes vulpes ) and small mustelids (the least weasel Mustela nivalis , and the stoat M. erminea ) [ 42 ]. Rodents constitute the majority of small mammals in this area, and voles are the dominant rodent species in this habitat, accounting for 90% of the small mammal community ([ 42 , 43 ]). The natural root vole population studied in this work is characterised by multi-annual, four-year abundance cycles [ 42 ]. The study began in 2005 during the vole population peak phase and was continued in 2006 during the decreasing phase of the vole population cycle (Fig. 1 ). Experimental setup We carried out an avian exclusion experiment from November 2005 to November 2006, i.e. spanning one winter. Three pairs of trapping grids (50 × 50 m, 1–4 km apart) were established in August 2005 in locations chosen to minimise variation in vegetation and topography among them. Each pair contained one experimental and one control plot, spaced approximately 300 m apart to reduce the probability of movement of voles between plots. The grids were open and were able to migrate. All grids were equipped with 36 permanent trap stations in a 6 × 6 grid with 10 m spacing. Each trap station consisted of one live trap. The population of voles was surveyed by live trapping at 2-month intervals throughout the experiment. Wooden live traps with metal doors were baited with oat seeds and checked twice a day, in the morning at 8:00 and in the evening at 19:00. The experiment comprised seven trapping sessions, held every two months starting from November (2005). Before the start of the experiment, we conducted one trapping session in August 2005 to compare the control and experimental grids in each location. During the winter/early spring sessions (January, March), when ambient temperatures were lowest, traps were opened only during the day (from 8:00 to 19:00) to reduce trap mortality. Likewise, during the summer session (July), when ambient temperatures were highest, traps were opened during the evening and night (from 18:00 to 8:00). Avian predation was excluded by covering the experimental grids with nylon netting (6 cm mesh size) at a height of 1.5 meters. This allowed predatory mammals such as weasels, stoats, and red foxes to access freely. The control grids, on the other hand, were accessible to all natural predators, including birds of prey. Each vole was individually marked by toe clipping when first captured. Upon each capture, sex, body mass (to the nearest 0.5 g) and reproductive condition of the vole were recorded before release at the point of capture. Statistical analyses To investigate whether preventing predation by raptors affected survival, we used individual capture histories to estimate apparent survival and its changes over the study period with a capture-mark-recapture (CMR) model. In addition, we analysed the effect of vole body mass on survival in the treatment and control plots, and finally we looked at the effect of raptor removal on vole population dynamics. Estimation of survival and population size We estimated monthly apparent survival using robust design models with Huggins conditional likelihood [ 44 ]. Within 4-day daily trapping sessions, populations are considered closed (no mortality or emigration is assumed) and survival was estimated between sessions. We applied information-theoretic model averaging procedure by generating a set of candidate models each including a subset of all considered parameters. In these models, survival ( S ) could be a function of treatment, location, time and an interaction of these variables, as well as age, cohort, sex and body mass of the individual, and the interaction of these variables with treatment; the effect of sex and body mass was additionally allowed to vary over time. Probability of capture ( p ) was modelled including the effects of location and sex, both interacting with time, and a linear effect of time within session (i.e. day of trapping session), possibly varying between sessions and location. Age and cohort were included as categorical variables. The effect of time (in primary periods, i.e. between trapping sessions) was included either as a categorical variable (i.e. session) or as a smooth spline function of time (B-spline based, with 3 or 4 degrees of freedom) to limit the number of parameters to be estimated. For the survival model, the categorical time variable interacting with location combined the last two sessions due to the parameter estimability problems in the last session. In the first session there was no net set up (no treatment effect), hence all plots share the parameters for “control” group. The effects of location and time, for the survival model also body mass and location-treatment interaction, and for capture probability model also time-location interaction, were included in all models considered. The survival parameter ( S ) reported refers to a 30-day period, i.e. monthly rate. The parameters for first capture and recapture were assumed to be equal ( p = c ), and temporary emigration was modelled as a uniformly random process ( γ′ = γ″ ). We limited the number of model terms (including interactions) in each model to a maximum of 11, furthermore we included only models in which all parameters were estimable and not at boundary (i.e. not at 0 or 1, and with non-zero variance). We assessed overdispersion in the models using parametric bootstrap [ 45 ], in which the estimated parameters are used to simulate capture histories for each individual in the original sample, then the model is fitted to these simulated data, and the deviance is recorded. Goodness of fit was assessed as the proportion of deviances from the simulations which exceed the observed deviance. Overdispersion ( ĉ ) was calculated as the proportion of the observed deviance to the mean of the deviances from simulations. We present averaged model predictions (from highest ranked models by small-sample Akaike Information Criterion, AIC c ), with 95% confidence intervals. To obtain the expected values and confidence intervals of the model-averaged predictions, we used simulations from models’ β parameters, with number of replicates from each model proportional to the model weight, ω . This method was used to calculate all reported point estimates and their uncertainty, including derived parameters such as population size and relative survival. The p -values were calculated by taking twice the proportion of sample values falling beyond the distance between the sample value and the null value. For the prediction, we took the actual mean body mass and sex ratio of the captured individuals at the prediction point, e.g. session and/or location (rather than the overall mean). The CMR modelling was conducted with the program Mark [ 70 ] version 10.1 (March 2023) through RMark interface (version 3.0.0; [ 71 ]) in the R environment (version 4.4.1; [ 72 ]). Figures were prepared with R base graphics. Body mass estimation Body mass was included in the CMR model as an individual covariate. Individual covariates need to be specified for each capture occasion, regardless of whether the individual was captured or not. Body mass changes dynamically throughout an individual's lifetime, and while its dynamics are individual-specific, visual examination of the data revealed that they follow a limited number of temporal patterns. To approximate the body mass change over the entire study period for each individual in the data set, we used latent class linear mixed-effects (LCME) models to assign each individual to a pattern of body mass change. This assignment was based on the individual's body mass at each capture occasion (trapping session) interacting with sex. We fitted models with 3 and 4 latent classes (i.e. body mass change patterns), with each model type replicated five times with different random starting values. For each model we derived a model weight from its Bayesian Information Criterion (BIC) [ 46 ]. For each class and sex, we calculated the mean body mass of individuals in each session. Rather than assigning an individual to a single class by taking the highest class probability, we calculated the mean weighted by class probabilities, according to the formula: \(\:{\stackrel{-}{m}}_{g,s,t}=\frac{\sum\:_{i\in\:{N}_{s,t}}{m}_{i,t}P({c}_{i}=g)}{\sum\:_{i\in\:{N}_{s,t}}P({c}_{i}=g)}\) , Eq. 1 where m̅ g,sex,t is the average body mass in class g’ , per each sex s , at occasion t; and given all individuals i that are of sex s and were caught at occasion t : m i,t is the body mass of individual i at occasion t , P(c i = g) is the probability of individuals’ belonging to class g . We calculated m̅ g,sex,t for each model and then averaged the values considering models’ BIC weights. Since one model (with 4 classes) had a weight of almost 100%, equations presented here refer to single model estimates. Next, to estimate body mass for each individual at all occasions, we adjusted m̅ g,sex,t by adding the mean difference of their actual body mass to the mean at the occasions they were captured. \(\:{\widehat{m}}_{i,t}=\sum\:_{g=1}^{k}\left({\stackrel{-}{m}}_{g,{sex}_{i},t}P\right({c}_{i}=g\left)\right)+\frac{\sum\:_{t}({\stackrel{-}{m}}_{g,{sex}_{i},t}-{m}_{i,t}){I}_{i}\left(t\right)}{\sum\:_{t}{I}_{i}\left(t\right)}\) , Eq. 2 The first part of Equation Error: Reference source not found is the average of mean body masses over all classes, g , for individual’s sex, and occasion t , weighted by the probabilities of individual membership in each of the latent classes. The second part is the mean difference between the respective mean body mass m̅ g,sex,t , and actual body mass, m of individual i at occasion t , where I i (t) is an indicator yielding 1 if individual i was caught at occasion t , or 0 otherwise. The correlation between observed and estimated body mass used for the CMR model was r p = 0.98 (Pearson's correlation, t = 262, df = 2715, p ≪ 0.0001). The above method provided body mass estimates that deviated considerably less from the actual values than the prediction from model parameters. LCME model fitting was performed using R package ‘lcmm’ (version 2.1.0; [ 47 ]). Declarations Ethical approval The study was conducted in accordance with Polish law. The animals were studied in their natural habitat, and the live-trapping and marking method was approved by the Third Local Ethics Committee for Animal Experimentation in Warsaw (Decision: WAW3/13/2004) and authorised under the licence from Biebrza National Park (Decision: No. 20/O/200)." Data availability The data and code used in this study is available on Zenodo at [https://doi.org/10.5281/zenodo.16034731]. Ethics approval and consent to participate All experimental procedures were approved by the Third Local Ethics Committee for Animal Experimentation in Warsaw, Poland (WAW3/13/2004), as well as under license from the Biebrza National Park (decision: No. 20/O/200). Consent for publication Not applicable. Availability of data and materials Data used in this study is available as supporting information. Competing interests The authors declare no conflict of interest. Funding The study was supported by the National Science Centre grant no. 2021/43/B/BZ8/0332 to KB and grant no. 2011/01/B/NZ8/04259 to ZB, as well as by the Forest Fund from the Polish State Forests (contract number: MZ.0290.1.2023) to ZB, in collaboration with the Biebrza National Park. Authors' contributions ZB and KAB conceived the ideas and designed the methodology; ZB collected the data; KAB analysed the data; ZB and KAB wrote the manuscript. All authors contributed critically to the drafts and gave final approval for publication. Acknowledgements The authors thank Kamil Krysiuk, Monika Wieczorek, and all the students who helped with data collection. Special thanks go to Andrew Carr, who contributed valuable comments and language corrections to an earlier draft of this paper. References Errington, P. L. Factors limiting higher vertebrate populations. Science 124 , 304–307 (1956). Errington, P. L. Predation and vertebrate populations. The Quarterly Review of Biology 21 , 144–177 (1946). Errington, P. L. The phenomenon of predation. American Scientist 51 , 180–192 (1963). Goszczyński, J. Connections between predatory birds and mammals and their prey. Acta Theriologica 22 , 399–430 (1977). Pearson, O. P. Predation. in Biology of new world Microtus (ed. Tamarin, R. 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The American Naturalist 123 , 125–133 (1984). White, G. C. & Burnham, K. P. Program MARK: survival estimation from populations of marked animals. Bird Study 46 , S120–S139 (1999). Laake, J. L. RMark: An R Interface for Analysis of Capture-Recapture Data with MARK . 25 https://apps-afsc.fisheries.noaa.gov/Publications/ProcRpt/PR2013-01.pdf (2013). R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2024). Tables Table 1 Model selection table. Rows are showing terms included in each capture-mark-recapture model, with number of parameters ( k ), relative AIC c value, and ‘Akaike weights’ ωAIC c . Only highest-ranked models with cumulative ‘Akaike weights’ ωAIC c ≤ 0.99 are included. Two components are given: model for capture probability, p and for survival S ; the remaining component models were identical. The ‘+’ denotes the presence of a term in a model. For model terms involving time, ‘[ 3 ]’ and ’[ 4 ]’ refer to the form of time effect included (as a smooth spline of 3rd or 4th order respectively), ‘+’ and ‘r’ denote time as a categorical variable, with ‘r’ indicating that the last two sessions have been combined. For consistency, ‘time’ in both components ( S and p ) refers to a session (i.e. primary sampling), and ‘session day’ refers to the ordinal day within each session (secondary sampling), which is different from the convention used in the program ‘Mark’, where ‘time’ in the capture probability model refers to secondary sampling. Survival model (S) Capture probability model (p) k ΔAIC c model weight ω AIC c location treatment time‍ body mass cohort location × treatment location × time treatment × time treatment × body mass time × body mass location sex location × time location × session day location × time × session day + + [ 4 ] + r [ 3 ] [ 3 ] + [ 4 ] + [ 3 ] 58 0.0 0.46 + + + + + r + [ 3 ] + [ 4 ] + [ 4 ] 63 0.9 0.30 + + + + r [ 3 ] + + [ 4 ] + [ 4 ] 63 2.8 0.11 + [ 4 ] + + + r [ 4 ] + [ 4 ] + [ 4 ] 67 3.8 0.07 Additional Declarations No competing interests reported. Supplementary Files SupportingInformationFiguresandTables.odt Figure S1. a) Survival of root voles at each site during the study, in treatment (no bird predation) and control plots, for intervals between sessions and scaled to a one-month period. Points show expected values, whiskers represent 95% confidence intervals. Estimates are averaged over a set of highest-ranked models (see Table 1). For better readability, consecutive points from the same plot are connected by a line, and points from the same session are slightly shifted apart along the horizontal axis. Figure S1. Body mass of voles captured in control and treatment plots, in each session and separately for each site. Violin graphs show the density distribution of body mass of individuals captured in each session, with dots indicating the median for control and treatment plots. or better readability, consecutive points from the same plot are connected by a line. Asterisks at top indicate significant difference (p ≤ 0.001, 0.01, 0.05 indicated by three, two and one symbols, respectively). Figure S2. Population size of root vole at each site during the study in treatment and control plots. Points show expected values, whiskers represent 95% confidence intervals. Estimates are averaged over a set of highest-ranked models (see Table 1). For readability, consecutive points from the same plot are connected by a line, and points from the same session are slightly shifted apart along the horizontal axis. Figure S3. Changes in body mass of captured voles. The recorded body mass of individuals is shown as points connected by a line along the time axis. The colours indicate the individuals seen in the treatment and control plots. If an individual was captured in consecutive sessions, the points are connected by a solid line, whereas if the captures were discontinuous (the individual appeared in the preceding and subsequent sessions), the points are connected by a dashed line. Each panel highlights one group as determined by the latent class linear mixed model. To allow for comparison, the remaining groups are shown in the background in light gray. Additional tables Table S1. Number of captures (i.e. individuals seen for the first time) and re-captures (second or subsequent record) in each session, broken down into treatment and control plots, locations and as an overall total. Table S1. Numbers of individuals caught in each plot in each trapping session. Table S2. Numbers of individuals in each cohort. A cohort as used in the capture-mark-recapture model context are individuals first observed during each session. Table S3. Body mass of captured individuals in each session, broken by sex. Table S4. Initial population size estimates at the beginning of the study (i.e. before netting), in control and treatment plots. Percentage difference refers to the mean population size of the pair of plots. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7099831","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489636330,"identity":"10d88dd3-acc0-4f9e-92d1-fcf50b78b3e9","order_by":0,"name":"Zbigniew Borowski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYFACHgglwcx84DBDAVzYghgtbAmHGQzgwhJEaGHgMWAmSot8A+/BxxUVh+0l23k+Hi4wsLFnkEh+JsG4A7cWxga+ZMMzZw4nzmbm3XB4hkFaYoNEmpkE4xncWpgZeMwkG9tuJ8iBtPAYHE5gkE4wu8HYhlsLG1SLvRwzzwOglv/2DNLp3/Bq4YFqYZzNzMMA1HKAsUE6B78tEsxAvzSc+Z84s5nNAOiX5MQ2+TflPxLx+EW+vffgw4aKNHuJ84cffy6osLPn5zm+2eDjDhucWoD+x/AdECQ24NaBAzCSrmUUjIJRMAqGLwAAK4RKA25A6OMAAAAASUVORK5CYII=","orcid":"","institution":"Forest Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Zbigniew","middleName":"","lastName":"Borowski","suffix":""},{"id":489636332,"identity":"c1e8e146-9535-4800-9f66-ed5ad636b912","order_by":1,"name":"Kamil A. Bartoń","email":"","orcid":"","institution":"Institute of Nature Conservation Polish Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kamil","middleName":"A.","lastName":"Bartoń","suffix":""}],"badges":[],"createdAt":"2025-07-11 09:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7099831/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7099831/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30214-y","type":"published","date":"2025-12-06T15:57:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87514431,"identity":"28471833-ea57-45c1-8df7-e45a5b60b7d2","added_by":"auto","created_at":"2025-07-24 16:09:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244950,"visible":true,"origin":"","legend":"\u003cp\u003e(a) View of the study site; (b) long-term population dynamics of the root vole Microtus oeconomus based on spring and autumn densities in Biebrza National Park in 1994-2008 [42]. The shaded area indicates the period of the avian exclusion experiment presented in this work.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7099831/v1/00554f7cf87ec75a6563fe55.jpg"},{"id":87514799,"identity":"6f6e3bfa-71cb-415e-802b-7fccd8f0c9a7","added_by":"auto","created_at":"2025-07-24 16:17:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73226,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1. (a) Average survival of root voles during the study, in treatment (no bird predation) and control plots, for intervals between sessions and scaled to a one-month period; and (b) relative survival for experimental treatment (ratio of survival in treatment and control plots). Points show expected values, whiskers represent 95% confidence intervals. Estimates are averaged over a set of highest-ranked models (see Table 1). Consecutive points from the same plot are connected by a line, points from the same session are shifted apart along the horizontal axis. Daily depth of snow cover is shown at the bottom axis with blue bars.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7099831/v1/693bbaa1d47f5d6205c4e7b6.jpg"},{"id":87514800,"identity":"0d9c1e7a-e878-40a5-94bd-1bb086a894a4","added_by":"auto","created_at":"2025-07-24 16:17:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126170,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2. Relationship between individual survival and body mass of root voles for between-sessions periods. Thick lines show expected values, shaded areas cover 95% confidence intervals. Estimates are based on the parameters of highest-ranked models and model-averaged (see Table 1). No treatment was applied in the first session. The body mass density distribution of voles (at the beginning of a given period) is shown at the bottom of each panel at x-axis (for control and treatment plots, stacked).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7099831/v1/80cad2ef9289f09e0e876748.jpg"},{"id":87514433,"identity":"b6bebde1-5036-4337-83af-9ab282162ab9","added_by":"auto","created_at":"2025-07-24 16:09:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46024,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3. Population size of root vole during the study in treatment and control plots. Points show expected values, whiskers represent 95% confidence intervals. Estimates are averaged over a set of highest-ranked models (see Table 1). For readability, consecutive points from the same plot are connected by a line, and points from the same session are slightly shifted apart along the horizontal axis.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7099831/v1/a8a82a0c4557481ddc637277.jpg"},{"id":97724082,"identity":"4171a646-3a0f-4e55-8a16-081635662535","added_by":"auto","created_at":"2025-12-08 16:11:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1331183,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7099831/v1/81257a67-43be-45fa-b994-7a51e801bb28.pdf"},{"id":87515711,"identity":"ebae04d4-82dd-46b0-959d-fddff7a70583","added_by":"auto","created_at":"2025-07-24 16:25:54","extension":"odt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":798553,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. a) Survival of root voles at each site during the study, in treatment (no bird predation) and control plots, for intervals between sessions and scaled to a one-month period. Points show expected values, whiskers represent 95% confidence intervals. Estimates are averaged over a set of highest-ranked models (see Table 1). For better readability, consecutive points from the same plot are connected by a line, and points from the same session are slightly shifted apart along the horizontal axis.\u003c/p\u003e\n\u003cp\u003eFigure S1. Body mass of voles captured in control and treatment plots, in each session and separately for each site. Violin graphs show the density distribution of body mass of individuals captured in each session, with dots indicating the median for control and treatment plots. or better readability, consecutive points from the same plot are connected by a line. Asterisks at top indicate significant difference (p ≤ 0.001, 0.01, 0.05 indicated by three, two and one symbols, respectively).\u003c/p\u003e\n\u003cp\u003eFigure S2. Population size of root vole at each site during the study in treatment and control plots. Points show expected values, whiskers represent 95% confidence intervals. Estimates are averaged over a set of highest-ranked models (see Table 1). For readability, consecutive points from the same plot are connected by a line, and points from the same session are slightly shifted apart along the horizontal axis.\u003c/p\u003e\n\u003cp\u003eFigure S3. Changes in body mass of captured voles. The recorded body mass of individuals is shown as points connected by a line along the time axis. The colours indicate the individuals seen in the treatment and control plots. If an individual was captured in consecutive sessions, the points are connected by a solid line, whereas if the captures were discontinuous (the individual appeared in the preceding and subsequent sessions), the points are connected by a dashed line. Each panel highlights one group as determined by the latent class linear mixed model. To allow for comparison, the remaining groups are shown in the background in light gray.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional tables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable S1. Number of captures (i.e. individuals seen for the first time) and re-captures (second or subsequent record) in each session, broken down into treatment and control plots, locations and as an overall total.\u003c/p\u003e\n\u003cp\u003eTable S1. Numbers of individuals caught in each plot in each trapping session.\u003c/p\u003e\n\u003cp\u003eTable S2. Numbers of individuals in each cohort. A cohort as used in the capture-mark-recapture model context are individuals first observed during each session.\u003c/p\u003e\n\u003cp\u003eTable S3. Body mass of captured individuals in each session, broken by sex.\u003c/p\u003e\n\u003cp\u003eTable S4. Initial population size estimates at the beginning of the study (i.e. before netting), in control and treatment plots. Percentage difference refers to the mean population size of the pair of plots.\u003c/p\u003e","description":"","filename":"SupportingInformationFiguresandTables.odt","url":"https://assets-eu.researchsquare.com/files/rs-7099831/v1/3b5620e1eb44d6267aec25be.odt"}],"financialInterests":"No competing interests reported.","formattedTitle":"Avian predation has the strongest impact on vole survival during winter and spring in temperate grasslands","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePredation pressure in seasonal environments can vary widely throughout the year, with winter being the critical period for both prey and predator populations. A prime example of prey in a multi-predator environment are small rodents, facing many natural enemies of widely varying sizes and hunting strategies. Unsurprisingly, predation has long been considered a factor responsible for shaping the dynamics of small rodent abundance (e.g. [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]). Small rodents often exhibit regular, periodic fluctuations in density, known as multi-year population cycles, characterised by a lagged density dependence on population growth rate (e.g. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]). Despite extensive research, the role of predators in small rodent populations remains uncertain, including their potential contribution to generating multiannual cycles of abundance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It has been posited that while predation may deepen and prolong the low phases of these cycles (e.g. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]), predation alone is neither essential nor sufficient to generate a cyclic dynamics in a prey population [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBirds of prey are assumed to exert a strong impact on small mammals, capable of either suppressing or amplifying cyclicity of their prey populations, and to be an important source of density-dependent mortality in vole populations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies involving radio-tracked voles have shown that raptors can achieve predation rates of approximately 30% during summer on experimental root vole (\u003cem\u003eMicrotus oeconomus\u003c/em\u003e) populations in Norway [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and on natural field vole populations in Finland [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Avian predation intensity is influenced by the availability of prey (phase of the cycle) and the environmental structure affecting visibility and hunting efficiency for birds [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e][Steen 1994, Ims and Andreassen 2000]. The risk of rodent predation by birds has been found to increase with environmental openness [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], with raptors preferring environments with low vegetation and high densities of voles, as this facilitates hunting [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the effect of mammalian sedentary specialist predators (small mustelids) on vole demography has been widely documented [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], studies analysing the role of avian predation are scarce. In contrast to small mustelids (\u003cem\u003eMustela nivalis\u003c/em\u003e and \u003cem\u003eM. erminea\u003c/em\u003e), which have often been indicated as a possible factor causing population cycles in northern populations (e.g., [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]), birds of prey, particularly migratory species like kestrel (\u003cem\u003eFalco tinnunculus tinnunculus\u003c/em\u003e), tend to display the fastest quantitative responses to changes in prey availability. Consequently, they may stabilise prey population dynamics rather than induce abundance cycles, often promoting spatial synchronisation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Some evidence suggests that, in certain populations, the predation pressure exerted by these birds in spring and early summer may complement the pressure from weasels, requiring a longer time lag to produce an abundance cycle [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMost research on the effect of predation has focused on multiannual dynamics (cycles) of small rodent populations, but the role of avian predators in shaping the seasonal dynamics remains unresolved. It is often assumed that raptors do not affect vole demographics during winter. The prevailing view is that vole populations in winter are more influenced by within-population factors, weather [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], or food availability [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Nevertheless, the impact of birds of prey on small mammal populations in autumn and winter remains to be adequately studied. In northern ecosystems, where most studies on this topic have been done, this shortage of research on bird predation on small mammals may be due to the fact that the snow cover provides a natural concealment for potential prey [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In temperate zone Europe, however, the snow cover is typically thinner and does not persist throughout the whole winter. Studies on the dietary habits of avian predators have indicated that voles constitute a significant portion of their winter diet [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, birds of prey may affect the population structure of small rodents through differential predation on different sex or age classes of prey [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo accurately assess the impact of predation, experimental approaches that either exclude or maintain predation pressure are essential. To date, only a few experiments have involved the exclusion of bird predation, and these have largely been limited to boreal ecosystems and enclosed populations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Results from these experiments indicate that in the boreal zone, reducing predation by birds led to a significant increase in the field vole (\u003cem\u003eMicrotus agrestis\u003c/em\u003e) population size and reversed the typical summer decline in their population [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Similar results were obtained in enclosed populations of voles and lemmings in aviaries, where excluding predation by both birds and mammalian predators [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] altered abundance dynamics and reversed decline phases.\u003c/p\u003e\u003cp\u003eIn our study, we examine the impact of avian predation on free-living root voles in temperate grassland habitats. Drawing on results from previous studies showing that predation can limit population growth of small mammals, we hypothesise that predation by birds of prey would substantially affect vole survival. Therefore, we predict that by preventing avian predation, vole survival would increase during the winter and spring, leading to a rise in the population density of voles at the start of the breeding season. Studies suggest that male voles [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and younger individuals [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] are generally more vulnerable to bird predation due to their higher locomotory activity and preference for open micro-habitats. Therefore, we considered that predation rate will be affected by sex and body mass of voles. To test these hypotheses, we conducted a spatially replicated field experiment involving exclusion of predation by birds, and monitoring vole populations throughout the year.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, we recorded 3984 captures of 1731 individuals, of which 59.2% (n\u0026thinsp;=\u0026thinsp;1025) were recaptured at least once (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the ESM). In each session and plot, a mean of 57.42 (\u0026plusmn;\u0026thinsp;15.1 s.d.) individuals were caught, with numbers ranging from 26 to 94 (see Table S2 in the ESM). An average individual was captured during 1.59 (\u0026plusmn;\u0026thinsp;0.98 s.d.) sessions, or on 2.3 (\u0026plusmn;\u0026thinsp;2.0 s.d.) occasions (days). A 63% of individuals (n\u0026thinsp;=\u0026thinsp;1093) were recorded in a single session, of which 831 \u0026ndash; only on one occasion (i.e. one day). The average period over which voles were seen (from first to last capture) was 48.73 (\u0026plusmn;\u0026thinsp;81.9 s.d.) days (with a maximum of 455 days). Nine individuals were recorded for the entire duration of the study (i.e. those from the first cohort who were also recaptured during the last trapping session), of which three in every session (see Table S3 in the ESM).\u003c/p\u003e\u003cp\u003e\u003cem\u003eModel selection\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe four best-ranked capture-mark-recapture models contributed a total of 99% of the model weight (\u003cem\u003eω\u003c/em\u003e) (1). They all included, in addition to parameters that were fixed to all models (for survival, \u003cem\u003eS\u003c/em\u003e: location, time and body mass; for capture probability, \u003cem\u003ep\u003c/em\u003e: location interacting with time), the treatment effect, interaction of location and time (for \u003cem\u003eS\u003c/em\u003e), time, body mass interacting with time, and location interacting with time within session (for \u003cem\u003ep\u003c/em\u003e). For survival, the treatment effect was included in interaction with either time or location. The time effect in these models was included either in a categorical form or as 3- or 4-degree spline function. Cohort and sex were only included in lower-ranked models. Therefore, model selection suggests that the experimental treatment was overall an important factor for survival, and both the survival and the effect of body mass on survival varied with time. We used AIC\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e for the above model ranking, since the bootstrap goodness-of-fit test showed no lack of fit of the models to our data (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6, \u003cem\u003eĉ\u003c/em\u003e = 1.02), indicating no need for quasi-likelihood adjustment. Six models had effectively non-zero weights (taken to be \u003cem\u003eω\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.001), and all the estimates reported below are averaged over these models.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSurvival\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFor about the first half of the study period (November to May), mean survival in the treatment plots was higher than in the respective control plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in the ESM). Relative survival, defined as a ratio of survival in treatment plots to that in control plots, was highest during winter, from November to March, with survival in control plots lower by 15%-22% than in treatment plots (p\u0026thinsp;\u0026le;\u0026thinsp;0.003). Overall, the temporal pattern of survival was similar between plots, but differed slightly in the timing and magnitude of peaks and troughs. Estimated survival increased between January and March (until May at Barwik site), followed by a decrease between May and July, and then another increase, which was highest at Barwik. A period of high survival coincided with a low average body mass, and conversely, a decrease in survival occurred when captured individuals were heavier (Figure S2 in the ESM).\u003c/p\u003e\u003cp\u003e\u003cem\u003eBody mass\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe effect of the experimental treatment on body mass was intermittent and varied between sites (Figure S2 in the ESM). Overall, the proportion of heavier individuals (above ca. 30 g) decreased in winter, followed by an increase that started in spring (Table S4 in the ESM). After this increase, between May and July, the higher proportion of heavier individuals persisted for a shorter period in the treated plots than in the control plots. However, this effect was only significant in Barwik and to a lesser extent in Losiowka.\u003c/p\u003e\u003cp\u003eRelationship between body mass and survival changed during the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Survival decreased with body mass between November and May, therefore the largest individuals faced highest mortality. The average body mass of individuals captured over that period was 24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 g (range 16\u0026ndash;44 g). In subsequent sessions (i.e. from May onwards) the models showed no effect of body mass on survival. This diminishing relationship between body mass and survival coincided with an increase in the proportion of larger individuals (in May, see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Figure S2 in the ESM), but as the share of larger individuals decreased again, the relationship remained flat.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePopulation size\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe estimated population size varied substantially during the study, as well as between locations (see Fig.\u0026nbsp;4; Figure S3 in the ESM). However, there were common patterns, which included a peak before the onset of winter, a winter decline, and a subsequent increase that continued through the end of the study. Initial population sizes, prior to netting, differed within each pair of plots, with the control populations being less numerous in each location (on average in 74.5 (67\u0026ndash;96) individuals in treatment, and 64.8 (43\u0026ndash;82) in control plots, the mean percentage difference was 14%, see Table S5 in the ESM). Relatively largest difference was between plots at Losiowka (25 individuals or 43%).\u003c/p\u003e\u003cp\u003eThe observed increase in population size that started in spring was stronger in plots where avian predation was excluded. It was particularly apparent between March and May, when population size in all treatment plots was clearly larger that in the corresponding control plots. At Barwik, the faster population increase in the treatment plot had a lasting effect, with the population size remaining consistently larger than in the control plot until the study was concluded. In the other plots, exclusion of bird predation did not cause clear long-term effect in terms of population size.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated the impact of avian predation on vole survival and, in turn, on population dynamics, by experimentally excluding bird access to free-living root voles throughout an entire year (from November 2005 to November 2006). This allowed us to analyse the role of raptors that prey on voles during breeding and non-breeding periods, including winter. In line with our predictions, predation by avian predators exerted considerable pressure on voles, reducing their survival by up to 20%. We found that bird predation was limiting vole abundance in spring. In contrast, during the primary breeding season in summer, predator exclusion had little effect on vole survival in the studied populations. Thus, the obtained results partially supported our prediction that avian predators would be a significant factor in vole mortality during the breeding season, similar to what has been observed in vole populations in northern Europe [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The differences between our observations and earlier reports most likely stem from the fact that in our study area birds that prey on voles were present throughout the winter [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which was not the case in the Norwegian [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and Finnish [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] studies. The importance of the avian predation component in vole dynamics has been demonstrated by experimental manipulations in Finland [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], where reducing the number of both birds of prey and small mustelids reversed the decline in the density of \u003cem\u003eMicrotus\u003c/em\u003e voles, whereas this did not occur when only small mustelids\u0026rsquo; population was reduced. Moreover, Norrdahl and Korpim\u0026auml;ki [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] found that, together with the high vole mortality caused by birds during the breeding season, overall densities of small rodents increased during the summer in predator-reduced areas, while decreasing in control areas. A similar pattern was observed in experimental enclosures in Finland, where the exclusion of all major predators (both avian and mammalian) led to significant differences in vole abundance between the enclosure and control populations, suggesting that predation plays a crucial role in maintaining the low phase of the population cycle in voles [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe found that vole survival increased where raptors were excluded, mainly in winter and spring. Previous studies have given relatively little attention to the impact of winter avian predation on voles, probably due to the limited number of wintering raptor species and the presence of snow cover. Some studies have focused on the survival of voles during winter, but without distinguishing between avian and mammalian predation: in Norway [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], where vole-eating raptors were almost absent, in Finland [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], where all predators were excluded in experimental plots, in Sweden, where all predators except small mustelids were excluded by fencing and netting, and in Poland [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], where small mustelids were excluded by fencing. In contrast to the Norwegian studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], results from experiments with predator exclusion in Finland and Sweden [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] confirm that predation during the winter-spring period is an important factor in the overwintering survival of voles.\u003c/p\u003e\u003cp\u003eNotably, there was a permanent snow cover in our study area from December to March (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which may have effectively protected voles against bird predation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, among the birds of prey present in our study area in winter were two species of owls, the tawny owl and the long-eared owl [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Owls may use acoustic signals to hunt small mammals under the snow, which has been experimentally demonstrated [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Moreover, during our study, the snow cover in winter was relatively thin; the maximum thickness did not exceed 15 cm, which could facilitate owls in locating and hunting concealed prey. The ability of owls to hunt successfully even during snowy winters has also been indirectly confirmed by analysis of their winter diet composition, which is dominated by Arvicolidae rodents (e.g. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]).\u003c/p\u003e\u003cp\u003eIn the present study, the survival rate of voles and their population dynamics were affected not only by experimental treatment but also showed considerable variation between the locations. Although the study plots were all situated in an open meadow, fine-scale local conditions may have influenced the predation and population dynamics of voles. For example, the structure and density of herbaceous vegetation, as well as the distance from the forest edge, can significantly impact bird predation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The most pronounced impact of avian predation on vole survival and population dynamics was observed in a location (Barwik) situated closest to the forest edge (300 m), whereas the other two locations were approximately 900 m away from the edge of the forest. Numerous studies have shown that environmental factors\u0026mdash;such as food availability, predators, shelters, competition, landscape structure, and micro-environmental variation\u0026mdash;have a strong influence on the population dynamics of small mammals [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The role of habitat has been demonstrated by the results of the experiment on field voles in Kielder Forest, Scotland, where the environmental effects of homogeneous forest plantations influenced the phase of the abundance cycle in the population of this species [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eContrary to our expectations, we did not detect sex-related differences in the susceptibility of voles to bird predation. Previous studies have indicated that males are generally more vulnerable to bird predation due to their higher levels of motor activity [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. However, we only considered the average effect of sex on survival throughout the year, i.e. without seasonal variation. Given that the study encompassed a period during which voles exhibited no sexual activity, and therefore when locomotor activity of both sexes was likely to be similar, this effect may have been small enough not to be detected.\u003c/p\u003e\u003cp\u003eWe found that the over-winter survival rate of voles decreased with their body mass. Although the relationship between the winter survival rates of small mammals and their body mass has been long recognised [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], it remains underexplored due to the limited number of studies focused on winter survival in general [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. As older individuals tend to be heavier [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], lower survival may be directly related to age. Additionally, during seasonal food shortages, the higher food requirements of heavier individuals may lead to increased risk of starvation and reduce their survival rates [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Furthermore, in some species, including root voles, a seasonal decrease in body mass during winter has been documented, presumably related to a reduction in energy requirements [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the exclusion of avian predation did not alter the effect of body mass on survival, an inspection of the capture histories revealed it improved winter survival of voles who had reduced their body mass at the onset of winter (cf. \u0026ldquo;class #2\u0026rdquo; in Figure S4 in the ESM). The majority of individuals from this seasonal cohort were not recorded after the November session and only survived through the winter if avian predation was removed. One of the possible explanations is that heavier voles need to forage for longer, making them more vulnerable to predation. In winter, heavier voles may also be in worse condition, making them more vulnerable to predation by birds. It should also be noted that since the survival rate of larger voles is overall low, the effect of avian predation on larger voles was relatively stronger than on those with smaller body mass.\u003c/p\u003e\u003cp\u003eInterestingly, we have observed a decrease in the survival rate of voles in both experimental and control plots, at the beginning of summer (from May to July). Therefore, this decline in survival cannot be explained by bird predation. This phenomenon may be analogous to what are known as \u0026ldquo;summer declines\u0026rdquo; in northern root vole populations, which are postulated to be caused by non-random prey selection by small mustelids [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. However, tracing the fate of voles from different seasonal cohorts, each adopting a different life-history strategy for growth and maturation, reveals that this decrease in summer apparent survival primarily affects voles born in the autumn of the previous year, which mature and gain weight rapidly in spring (cf. \u0026ldquo;class #3\u0026rdquo; in Figure S4 in the ESM), suggesting that a major contributor to this decline is [indeed] mortality rather than emigration.\u003c/p\u003e\u003cp\u003eConsidering the seasonal population abundance dynamics, the impact of bird predation on root voles became apparent in early spring (March to May), when abundance increased briefly in plots with predation removed. This coincides with the arrival and breeding season of bird species that prey on voles [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In early spring, meadows provide the fewest shelters, making voles more vulnerable to attacks by birds of prey. Additionally, at the beginning of the breeding season, the open wet meadow in our study area is typically flooded, and vegetation is less dense, making it easier for predators to locate rodents. When vegetation becomes dense around July, it acts as a cover for voles from bird predators. This finding aligns with the results of earlier studies showing that the impact of birds of prey on the vole population is greatest in spring [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWinter conditions may be a key determinant of vole survival and, in consequence, their density at the onset of the breeding season, influencing both seasonal and multiannual population dynamics [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Erlinge et al. [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] showed that high winter predation can outweigh the summer reproduction of vole populations, stabilising population dynamics and dampening its cyclicity. The results of our study add another piece to the puzzle regarding the causes of vole population crashes in winter. Although previously thought to be mainly driven by climatic conditions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], we demonstrate that part of the winter mortality of voles can be caused by avian predation, even during the persistence of snow cover. We also show that it is during the winter-spring period that avian predation has the greatest impact on vole survival and abundance. The changing climate will likely affect vole survival during the winter-spring period by altering the timing of when birds exert the highest predation pressure on voles, as it has already led to reduced rainfall and snow cover. This can make it easier for birds to prey on small mammals during winter, but may decrease their hunting success in early spring.\u003c/p\u003e\u003cp\u003eFinally, it should be noted that avian predation can vary significantly depending on the phase of the population cycle, often being several times higher during the decline phase (e.g. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]). Our study was carried out during the peak of the population [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], thus to fully understand the role birds of prey play in shaping vole population dynamics, further research is needed to examine this topic during the decline phase of the vole population.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cem\u003eStudy area\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe study was conducted in the Lower Basin of the Biebrza National Park, NE Poland (53\u0026deg;36\u0026prime;18\u0026Prime;N, 22\u0026deg;55\u0026prime;36\u0026Prime;E). The study area is located in a homogeneous sedge wetland with the vegetation dominated by plants of the Cyperaceae family. The main plant species in the Park is the fibrous tussock sedge \u003cem\u003eCarex appropinquata\u003c/em\u003e, which covers 85% of the area and forms hummock\u0026ndash;hollow structures with tufts up to 1 m high (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In places, the sedge meadow is interspersed with shrubbery, including willows, birches and alders, indicative of an early stage of secondary succession. The wetland has a seasonal water regime with the highest level in spring when flooding is frequent. However, no spring flooding was recorded during the study period. The climate of the area combines continental and subboreal features, with long winters (\u0026gt;\u0026thinsp;100 days), a short and early spring, and a short growing season (77\u0026ndash;85 days). The coldest month is February and the warmest is July, and the total annual precipitation is 550 mm. The winter (2005/2006) was characterised by permanent snow cover from December to the end of March. The meteorological data on the duration and thickness of the snow cover were obtained from the Biebrza National Park meteorological station.\u003c/p\u003e\u003cp\u003eThe community of diurnal birds of prey observed near the trapping grids included the common buzzard (\u003cem\u003eButeo buteo\u003c/em\u003e), rough-legged buzzard (\u003cem\u003eB. lagopus\u003c/em\u003e), Western marsh harrier (\u003cem\u003eCircus aeruginosus\u003c/em\u003e), hen harrier (\u003cem\u003eC. cyaneus\u003c/em\u003e), and Montagu\u0026rsquo;s harrier (\u003cem\u003eC. pygargus\u003c/em\u003e), as well as the lesser spotted eagle (\u003cem\u003eClanga pomarina\u003c/em\u003e). The highest numbers of diurnal vole-eating birds were recorded in spring (April-May) and autumn (November), and the lowest in early autumn (September) and winter (January-February) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The area is also home to a resident year-round population of tawny owls (\u003cem\u003eStrix aluco\u003c/em\u003e) and long-eared owls (\u003cem\u003eAsio otus\u003c/em\u003e), as well as seasonally migrating short-eared owls (\u003cem\u003eAsio flammeus\u003c/em\u003e). The main mammalian predators of the voles were the red fox (\u003cem\u003eVulpes vulpes\u003c/em\u003e) and small mustelids (the least weasel \u003cem\u003eMustela nivalis\u003c/em\u003e, and the stoat \u003cem\u003eM. erminea\u003c/em\u003e) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRodents constitute the majority of small mammals in this area, and voles are the dominant rodent species in this habitat, accounting for 90% of the small mammal community ([\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]). The natural root vole population studied in this work is characterised by multi-annual, four-year abundance cycles [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The study began in 2005 during the vole population peak phase and was continued in 2006 during the decreasing phase of the vole population cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eExperimental setup\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe carried out an avian exclusion experiment from November 2005 to November 2006, i.e. spanning one winter. Three pairs of trapping grids (50 \u0026times; 50 m, 1\u0026ndash;4 km apart) were established in August 2005 in locations chosen to minimise variation in vegetation and topography among them. Each pair contained one experimental and one control plot, spaced approximately 300 m apart to reduce the probability of movement of voles between plots. The grids were open and were able to migrate. All grids were equipped with 36 permanent trap stations in a 6 \u0026times; 6 grid with 10 m spacing. Each trap station consisted of one live trap. The population of voles was surveyed by live trapping at 2-month intervals throughout the experiment. Wooden live traps with metal doors were baited with oat seeds and checked twice a day, in the morning at 8:00 and in the evening at 19:00. The experiment comprised seven trapping sessions, held every two months starting from November (2005). Before the start of the experiment, we conducted one trapping session in August 2005 to compare the control and experimental grids in each location. During the winter/early spring sessions (January, March), when ambient temperatures were lowest, traps were opened only during the day (from 8:00 to 19:00) to reduce trap mortality. Likewise, during the summer session (July), when ambient temperatures were highest, traps were opened during the evening and night (from 18:00 to 8:00).\u003c/p\u003e\u003cp\u003eAvian predation was excluded by covering the experimental grids with nylon netting (6 cm mesh size) at a height of 1.5 meters. This allowed predatory mammals such as weasels, stoats, and red foxes to access freely. The control grids, on the other hand, were accessible to all natural predators, including birds of prey. Each vole was individually marked by toe clipping when first captured. Upon each capture, sex, body mass (to the nearest 0.5 g) and reproductive condition of the vole were recorded before release at the point of capture.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo investigate whether preventing predation by raptors affected survival, we used individual capture histories to estimate apparent survival and its changes over the study period with a capture-mark-recapture (CMR) model. In addition, we analysed the effect of vole body mass on survival in the treatment and control plots, and finally we looked at the effect of raptor \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eremoval\u003c/span\u003e on vole population dynamics.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEstimation of survival and population size\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWe estimated monthly apparent survival using robust design models with Huggins conditional likelihood [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Within 4-day daily trapping sessions, populations are considered closed (no mortality or emigration is assumed) and survival was estimated between sessions. We applied information-theoretic model averaging procedure by generating a set of candidate models each including a subset of all considered parameters. In these models, survival (\u003cem\u003eS\u003c/em\u003e) could be a function of treatment, location, time and an interaction of these variables, as well as age, cohort, sex and body mass of the individual, and the interaction of these variables with treatment; the effect of sex and body mass was additionally allowed to vary over time. Probability of capture (\u003cem\u003ep\u003c/em\u003e) was modelled including the effects of location and sex, both interacting with time, and a linear effect of time within session (i.e. day of trapping session), possibly varying between sessions and location. Age and cohort were included as categorical variables. The effect of time (in primary periods, i.e. between trapping sessions) was included either as a categorical variable (i.e. session) or as a smooth spline function of time (B-spline based, with 3 or 4 degrees of freedom) to limit the number of parameters to be estimated. For the survival model, the categorical time variable interacting with location combined the last two sessions due to the parameter estimability problems in the last session. In the first session there was no net set up (no treatment effect), hence all plots share the parameters for \u0026ldquo;control\u0026rdquo; group. The effects of location and time, for the survival model also body mass and location-treatment interaction, and for capture probability model also time-location interaction, were included in all models considered. The survival parameter (\u003cem\u003eS\u003c/em\u003e) reported refers to a 30-day period, i.e. monthly rate. The parameters for first capture and recapture were assumed to be equal (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ec\u003c/em\u003e), and temporary emigration was modelled as a uniformly random process (\u003cem\u003eγ\u0026prime;\u003c/em\u003e = \u003cem\u003eγ\u0026Prime;\u003c/em\u003e). We limited the number of model terms (including interactions) in each model to a maximum of 11, furthermore we included only models in which all parameters were estimable and not at boundary (i.e. not at 0 or 1, and with non-zero variance).\u003c/p\u003e\u003cp\u003eWe assessed overdispersion in the models using parametric bootstrap [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], in which the estimated parameters are used to simulate capture histories for each individual in the original sample, then the model is fitted to these simulated data, and the deviance is recorded. Goodness of fit was assessed as the proportion of deviances from the simulations which exceed the observed deviance. Overdispersion (\u003cem\u003eĉ\u003c/em\u003e) was calculated as the proportion of the observed deviance to the mean of the deviances from simulations. We present averaged model predictions (from highest ranked models by small-sample Akaike Information Criterion, AIC\u003csub\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sub\u003e), with 95% confidence intervals. To obtain the expected values and confidence intervals of the model-averaged predictions, we used simulations from models\u0026rsquo; \u003cem\u003eβ\u003c/em\u003e parameters, with number of replicates from each model proportional to the model weight, \u003cem\u003eω\u003c/em\u003e. This method was used to calculate all reported point estimates and their uncertainty, including derived parameters such as population size and relative survival. The \u003cem\u003ep\u003c/em\u003e-values were calculated by taking twice the proportion of sample values falling beyond the distance between the sample value and the null value. For the prediction, we took the actual mean body mass and sex ratio of the captured individuals at the prediction point, e.g. session and/or location (rather than the overall mean). The CMR modelling was conducted with the program Mark [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] version 10.1 (March 2023) through RMark interface (version 3.0.0; [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]) in the R environment (version 4.4.1; [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]). Figures were prepared with R base graphics.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBody mass estimation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eBody mass was included in the CMR model as an individual covariate. Individual covariates need to be specified for each capture occasion, regardless of whether the individual was captured or not. Body mass changes dynamically throughout an individual's lifetime, and while its dynamics are individual-specific, visual examination of the data revealed that they follow a limited number of temporal patterns. To approximate the body mass change over the entire study period for each individual in the data set, we used latent class linear mixed-effects (LCME) models to assign each individual to a pattern of body mass change. This assignment was based on the individual's body mass at each capture occasion (trapping session) interacting with sex. We fitted models with 3 and 4 latent classes (i.e. body mass change patterns), with each model type replicated five times with different random starting values. For each model we derived a model weight from its Bayesian Information Criterion (BIC) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor each class and sex, we calculated the mean body mass of individuals in each session. Rather than assigning an individual to a single class by taking the highest class probability, we calculated the mean weighted by class probabilities, according to the formula:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{m}}_{g,s,t}=\\frac{\\sum\\:_{i\\in\\:{N}_{s,t}}{m}_{i,t}P({c}_{i}=g)}{\\sum\\:_{i\\in\\:{N}_{s,t}}P({c}_{i}=g)}\\)\u003c/span\u003e\u003c/span\u003e, Eq.\u0026nbsp;1\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003em̅\u003c/em\u003e\u003csub\u003e\u003cem\u003eg,sex,t\u003c/em\u003e\u003c/sub\u003e is the average body mass in class \u003cem\u003eg\u0026rsquo;\u003c/em\u003e, per each sex \u003cem\u003es\u003c/em\u003e, at occasion \u003cem\u003et;\u003c/em\u003e and given all individuals \u003cem\u003ei\u003c/em\u003e that are of sex \u003cem\u003es\u003c/em\u003e and were caught at occasion \u003cem\u003et\u003c/em\u003e: \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e is the body mass of individual \u003cem\u003ei\u003c/em\u003e at occasion \u003cem\u003et\u003c/em\u003e, \u003cem\u003eP(c\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e= g)\u003c/em\u003e is the probability of individuals\u0026rsquo; belonging to class \u003cem\u003eg\u003c/em\u003e. We calculated \u003cem\u003em̅\u003c/em\u003e\u003csub\u003e\u003cem\u003eg,sex,t\u003c/em\u003e\u003c/sub\u003e for each model and then averaged the values considering models\u0026rsquo; BIC weights. Since one model (with 4 classes) had a weight of almost 100%, equations presented here refer to single model estimates.\u003c/p\u003e\u003cp\u003eNext, to estimate body mass for each individual at all occasions, we adjusted \u003cem\u003em̅\u003c/em\u003e\u003csub\u003e\u003cem\u003eg,sex,t\u003c/em\u003e\u003c/sub\u003e by adding the mean difference of their actual body mass to the mean at the occasions they were captured.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{m}}_{i,t}=\\sum\\:_{g=1}^{k}\\left({\\stackrel{-}{m}}_{g,{sex}_{i},t}P\\right({c}_{i}=g\\left)\\right)+\\frac{\\sum\\:_{t}({\\stackrel{-}{m}}_{g,{sex}_{i},t}-{m}_{i,t}){I}_{i}\\left(t\\right)}{\\sum\\:_{t}{I}_{i}\\left(t\\right)}\\)\u003c/span\u003e\u003c/span\u003e, Eq.\u0026nbsp;2\u003c/p\u003e\u003cp\u003eThe first part of Equation Error: Reference source not found is the average of mean body masses over all classes, \u003cem\u003eg\u003c/em\u003e, for individual\u0026rsquo;s sex, and occasion \u003cem\u003et\u003c/em\u003e, weighted by the probabilities of individual membership in each of the latent classes. The second part is the mean difference between the respective mean body mass \u003cem\u003em̅\u003c/em\u003e\u003csub\u003e\u003cem\u003eg,sex,t\u003c/em\u003e\u003c/sub\u003e, and actual body mass, \u003cem\u003em\u003c/em\u003e of individual \u003cem\u003ei\u003c/em\u003e at occasion \u003cem\u003et\u003c/em\u003e, where \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(t)\u003c/em\u003e is an indicator yielding 1 if individual \u003cem\u003ei\u003c/em\u003e was caught at occasion \u003cem\u003et\u003c/em\u003e, or 0 otherwise.\u003c/p\u003e\u003cp\u003eThe correlation between observed and estimated body mass used for the CMR model was \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e = 0.98 (Pearson's correlation, \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;262, df\u0026thinsp;=\u0026thinsp;2715, \u003cem\u003ep\u003c/em\u003e ≪ 0.0001). The above method provided body mass estimates that deviated considerably less from the actual values than the prediction from model parameters. LCME model fitting was performed using R package \u0026lsquo;lcmm\u0026rsquo; (version 2.1.0; [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with Polish law. The animals were studied in their natural habitat, and the live-trapping and marking method was approved by the Third Local Ethics Committee for Animal Experimentation in Warsaw (Decision: WAW3/13/2004) and authorised under the licence from Biebrza National Park (Decision: No. 20/O/200).\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and code used in this study is available on Zenodo at \u0026nbsp; [https://doi.org/10.5281/zenodo.16034731].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures were approved by the Third Local Ethics Committee for Animal Experimentation in Warsaw, Poland (WAW3/13/2004), as well as under license from the Biebrza National Park (decision: No. 20/O/200).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used in this study is available as supporting information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the National Science Centre grant no. 2021/43/B/BZ8/0332 to KB and grant no. 2011/01/B/NZ8/04259 to ZB, as well as by the Forest Fund from the Polish State Forests (contract number: MZ.0290.1.2023) to ZB, in collaboration with the Biebrza National Park.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZB and KAB conceived the ideas and designed the methodology; ZB collected the data; KAB analysed the data; ZB and KAB wrote the manuscript. All authors contributed critically to the drafts and gave final approval for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Kamil Krysiuk, Monika Wieczorek, and all the students who helped with data collection. Special thanks go to Andrew Carr, who contributed valuable comments and language corrections to an earlier draft of this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eErrington, P. L. Factors limiting higher vertebrate populations. \u003cem\u003eScience\u003c/em\u003e\u003cstrong\u003e124\u003c/strong\u003e, 304\u0026ndash;307 (1956).\u003c/li\u003e\n\u003cli\u003eErrington, P. L. Predation and vertebrate populations. \u003cem\u003eThe Quarterly Review of Biology\u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e, 144\u0026ndash;177 (1946).\u003c/li\u003e\n\u003cli\u003eErrington, P. L. 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Effects of habitat fragmentation on meadow vole (Microtus pennsylvanicus) population dynamics in experiment landscape patches. \u003cem\u003eLandscape Ecol\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 63\u0026ndash;76 (1997).\u003c/li\u003e\n\u003cli\u003eErgon, T., Lambin, X. \u0026amp; Stenseth, N. Chr. Life-history traits of voles in a fluctuating population respond to the immediate environment. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e411\u003c/strong\u003e, 1043\u0026ndash;1045 (2001).\u003c/li\u003e\n\u003cli\u003eSouthern, H. N. \u0026amp; Lowe, V. P. W. The Pattern of Distribution of Prey and Predation in Tawny Owl Territories. \u003cem\u003eThe Journal of Animal Ecology\u003c/em\u003e\u003cstrong\u003e37\u003c/strong\u003e, 75 (1968).\u003c/li\u003e\n\u003cli\u003eKorpim\u0026auml;ki, E. Prey choice strategies of the kestrel Falco tinnunculus in relation to available small mammals and other Finnish birds of prey. \u003cem\u003eAnnales Zoologici Fennici\u003c/em\u003e\u003cstrong\u003e22\u003c/strong\u003e, 91\u0026ndash;104 (1985).\u003c/li\u003e\n\u003cli\u003eIverson, S. L. \u0026amp; Turner, B. N. Winter Weight Dynamics in Microtus Pennsylvanicus. \u003cem\u003eEcology\u003c/em\u003e\u003cstrong\u003e55\u003c/strong\u003e, 1030\u0026ndash;1041 (1974).\u003c/li\u003e\n\u003cli\u003eWhitney, P. Population Ecology of Two Sympatric Species of Subarctic Microtine Rodents. \u003cem\u003eEcological Monographs\u003c/em\u003e\u003cstrong\u003e46\u003c/strong\u003e, 85\u0026ndash;104 (1976).\u003c/li\u003e\n\u003cli\u003eJackson, D. M., Trayhurn, P. \u0026amp; Speakman, J. R. Associations between energetics and over‐winter survival in the short‐tailed field vole \u003cem\u003eMicrotus agrestis\u003c/em\u003e. \u003cem\u003eJournal of Animal Ecology\u003c/em\u003e\u003cstrong\u003e70\u003c/strong\u003e, 633\u0026ndash;640 (2001).\u003c/li\u003e\n\u003cli\u003eErgon, T., Speakman, J. R., Scantlebury, M., Cavanagh, R. \u0026amp; Lambin, X. Optimal body size and energy expenditure during winter: why are voles smaller in declining populations? \u003cem\u003eThe American Naturalist\u003c/em\u003e\u003cstrong\u003e163\u003c/strong\u003e, 442\u0026ndash;457 (2004).\u003c/li\u003e\n\u003cli\u003eKorpela, K. \u003cem\u003eet al.\u003c/em\u003e Predator\u0026ndash;vole interactions in northern Europe: the role of small mustelids revised. \u003cem\u003eProc. R. Soc. B.\u003c/em\u003e\u003cstrong\u003e281\u003c/strong\u003e, 20142119 (2014).\u003c/li\u003e\n\u003cli\u003eTaitt, M. J. \u0026amp; Krebs, C. J. Predation, cover, and food manipulations during a spring decline of Microtus townsendii. \u003cem\u003eJournal of Animal Ecology\u003c/em\u003e\u003cstrong\u003e50\u003c/strong\u003e, 837\u0026ndash;848 (1983).\u003c/li\u003e\n\u003cli\u003eZub, K., Borowski, Z., Szafrańska, P. A., Wieczorek, M. \u0026amp; Konarzewski, M. Lower body mass and higher metabolic rate enhance winter survival in root voles, Microtus oeconomus. \u003cem\u003eBiological Journal of the Linnean Society\u003c/em\u003e\u003cstrong\u003e113\u003c/strong\u003e, 297\u0026ndash;309 (2014).\u003c/li\u003e\n\u003cli\u003eErlinge, S. \u003cem\u003eet al.\u003c/em\u003e Can vertebrate predators regulate their prey? \u003cem\u003eThe American Naturalist\u003c/em\u003e\u003cstrong\u003e123\u003c/strong\u003e, 125\u0026ndash;133 (1984).\u003c/li\u003e\n\u003cli\u003eWhite, G. C. \u0026amp; Burnham, K. P. Program MARK: survival estimation from populations of marked animals. \u003cem\u003eBird Study\u003c/em\u003e\u003cstrong\u003e46\u003c/strong\u003e, S120\u0026ndash;S139 (1999).\u003c/li\u003e\n\u003cli\u003eLaake, J. L. \u003cem\u003eRMark: An R Interface for Analysis of Capture-Recapture Data with MARK\u003c/em\u003e. 25 https://apps-afsc.fisheries.noaa.gov/Publications/ProcRpt/PR2013-01.pdf (2013).\u003c/li\u003e\n\u003cli\u003eR Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2024).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cdiv class=\"SimplePara\"\u003eModel selection table. Rows are showing terms included in each capture-mark-recapture model, with number of parameters (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ek\u003c/span\u003e), relative AIC\u003csub\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ec\u003c/span\u003e\u003c/sub\u003e value, and \u0026lsquo;Akaike weights\u0026rsquo; \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eωAIC\u003c/span\u003e\u003csub\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ec\u003c/span\u003e\u003c/sub\u003e. Only highest-ranked models with cumulative \u0026lsquo;Akaike weights\u0026rsquo; ωAIC\u003csub\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ec\u003c/span\u003e\u003c/sub\u003e \u0026le; 0.99 are included. Two components are given: model for capture probability, \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e and for survival \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eS\u003c/span\u003e; the remaining component models were identical. The \u0026lsquo;+\u0026rsquo; denotes the presence of a term in a model. For model terms involving time, \u0026lsquo;[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u0026rsquo; and \u0026rsquo;[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u0026rsquo; refer to the form of time effect included (as a smooth spline of 3rd or 4th order respectively), \u0026lsquo;+\u0026rsquo; and \u0026lsquo;r\u0026rsquo; denote time as a categorical variable, with \u0026lsquo;r\u0026rsquo; indicating that the last two sessions have been combined. For consistency, \u0026lsquo;time\u0026rsquo; in both components (\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003eS\u003c/span\u003e and \u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ep\u003c/span\u003e) refers to a session (i.e. primary sampling), and \u0026lsquo;session day\u0026rsquo; refers to the ordinal day within each session (secondary sampling), which is different from the convention used in the program \u0026lsquo;Mark\u0026rsquo;, where \u0026lsquo;time\u0026rsquo; in the capture probability model refers to secondary sampling.\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"18\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSurvival model (S)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u003cdiv class=\"SimplePara\"\u003eCapture probability model (p)\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\" morerows=\"1\" rowspan=\"2\"\u003e\u003cdiv class=\"SimplePara\"\u003ek\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\" morerows=\"1\" rowspan=\"2\"\u003e\u003cdiv class=\"SimplePara\"\u003eΔAIC\u003csub\u003e\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ec\u003c/span\u003e\u003c/sub\u003e\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\" morerows=\"1\" rowspan=\"2\"\u003e\u003cdiv class=\"SimplePara\"\u003emodel\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003eweight\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003eω\u003csub\u003eAIC\u003cspan type=\"Italic\" class=\"Italic\" name=\"Emphasis\"\u003ec\u003c/span\u003e\u003c/sub\u003e\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003elocation\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003etreatment\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003etime\u0026zwj;\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003ebody\u0026nbsp;mass\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cdiv class=\"SimplePara\"\u003ecohort\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cdiv class=\"SimplePara\"\u003elocation\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026times;\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003etreatment\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003elocation\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026times;\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003etime\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cdiv class=\"SimplePara\"\u003etreatment\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026times;\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003etime\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cdiv class=\"SimplePara\"\u003etreatment\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026times;\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003ebody\u0026nbsp;mass\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cdiv class=\"SimplePara\"\u003etime\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026times;\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003ebody\u0026nbsp;mass\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cdiv class=\"SimplePara\"\u003elocation\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cdiv class=\"SimplePara\"\u003esex\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cdiv class=\"SimplePara\"\u003elocation\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026times;\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003etime\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cdiv class=\"SimplePara\"\u003elocation\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026times;\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003esession day\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cdiv class=\"SimplePara\"\u003elocation\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026times;\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003etime\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003e\u0026times;\u003c/div\u003e\u003cdiv class=\"SimplePara\"\u003esession day\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cdiv class=\"SimplePara\"\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cdiv class=\"SimplePara\"\u003er\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cdiv class=\"SimplePara\"\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cdiv class=\"SimplePara\"\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cdiv class=\"SimplePara\"\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cdiv class=\"SimplePara\"\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cdiv class=\"SimplePara\"\u003e58\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e\u003cdiv class=\"SimplePara\"\u003e0.0\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e\u003cdiv 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Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7099831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7099831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredation is widely acknowledged as an important factor affecting small rodent populations, yet its specific impact on their dynamics is not fully understood. In seasonal environments, winter and early spring are critical periods for small mammal populations, as environmental stressors \u0026ndash; including predation \u0026ndash; coincide with an inability to offset mortality through reproduction. Although birds of prey are major rodent predators, their effect on prey populations during this period remains poorly quantified. To address this, we conducted a year-round field experiment in temperate grasslands, excluding avian predators from root vole (\u003cem\u003eMicrotus oeconomus\u003c/em\u003e) populations using net-covered plots in three locations. Vole survival and population size were assessed using capture\u0026ndash;mark\u0026ndash;recapture method, considering effects of sex and body mass of individuals.\u003c/p\u003e\u003cp\u003eOur results show that avian predation significantly reduced vole survival during winter and spring (November\u0026ndash;May), increasing mortality by up to 22%, even when under snow cover. In contrast, no effect was detected during the rest of the year, and bird predation did not influence seasonal population dynamics. Overwinter survival was negatively associated with body mass, with larger individuals experiencing higher mortality; this pattern was not modified by predation exclusion.\u003c/p\u003e\u003cp\u003eThese findings demonstrate that avian predators exert substantial seasonal pressure on vole survival, contributing to winter\u0026ndash;spring population declines. However, the influence of bird predation appears limited in shaping long-term population dynamics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Avian predation has the strongest impact on vole survival during winter and spring in temperate grasslands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 16:09:49","doi":"10.21203/rs.3.rs-7099831/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-06T11:00:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T13:44:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267292736326473472737043738607662132460","date":"2025-07-29T15:59:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310014276779319555860958863708175627430","date":"2025-07-28T13:21:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T11:01:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329877434570641481485565662335850950569","date":"2025-07-23T09:02:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192584211300979570009843169511436067578","date":"2025-07-23T06:51:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-22T15:28:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-22T14:56:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-22T13:12:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-18T12:23:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-18T12:12:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3af67193-16ff-4117-99a7-51cfa88178ca","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51982442,"name":"Biological sciences/Ecology"},{"id":51982443,"name":"Earth and environmental sciences/Ecology"},{"id":51982444,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2025-12-08T16:06:29+00:00","versionOfRecord":{"articleIdentity":"rs-7099831","link":"https://doi.org/10.1038/s41598-025-30214-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-06 15:57:55","publishedOnDateReadable":"December 6th, 2025"},"versionCreatedAt":"2025-07-24 16:09:49","video":"","vorDoi":"10.1038/s41598-025-30214-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30214-y","workflowStages":[]},"version":"v1","identity":"rs-7099831","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7099831","identity":"rs-7099831","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}