• Capture–mark–recapture;
  • dispersal;
  • immigration;
  • recruitment;
  • survival


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • 1
    Commensal mammals live in habitats that appear to provide both benefits and costs compared with natural and semi-natural (non-commensal) habitats. These commensal habitats offer potentially rich food resources but are also characterized by instability in time and space. We expected to demonstrate that animals in these habitats have high reproductive rates to counter high mortality rates and show flexibility in spatial organization. House mice (Mus musculus domesticus) are unusual because they are able to persist entirely in both commensal and non-commensal habitats, and so can provide a test of the distinctiveness of commensal populations.
  • 2
    We studied populations of commensal house mice on two neighbouring farms in North Yorkshire, UK, for 2 years by capture–mark–recapture using the robust design. A total of 568 house mice were captured, with a total of 1053 recaptures. Population size varied from nine to 93 individuals, estimated with closed population models. Apparent survival was surprisingly low (0·54 per month) and was best modelled as constant across age, sex and time.
  • 3
    In situ reproductive recruitment occurred throughout the study and was numerically more important than immigration. Immigration was important during only two intervals and was probably from untrappable areas within the study site. Breeding throughout the year allowed the population to persist despite low survival rates. The results suggested that births and deaths had more influence on the overall population dynamics than movement.
  • 4
    The population was divided into subgroups to represent the territorial, demic structure known to be present in commensal house mouse populations. Dispersal between subgroups within the population was limited, representing only 6·6% of recaptures. The low rates of dispersal suggested that house mice responded to their environment as consisting of aggregated patches of suitable habitat.
  • 5
    In comparison, non-commensal house mice have lower mortality, seasonal breeding, and individuals move more frequently and further than commensal house mice.
  • 6
    These differences illustrate the responses of house mice to the specific opportunities and demands of commensal habitats and demonstrate the importance of a flexible life-history strategy for animals exploiting these habitats.


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Understanding the population dynamics of animals was one of the factors motivating ecologists at the beginning of the twentieth century (e.g. Elton 1927). Since then, small mammals have been commonly used as model systems because of their convenient size and short generation times (Krebs 1998). However, almost all studies have concentrated on natural or semi-natural habitats and even studies in urban areas have focused mostly on the vegetated landscapes of parks and gardens, which, in Britain, have similar small mammal communities to agricultural habitats (Dickman & Doncaster 1987, 1989; Baker et al. 2003). The habitats in which commensal small mammals live are markedly different from this. We term these ‘commensal habitats’ and they include areas in and around human dwellings, farms, buildings, vehicles, food stores and waste areas.

Although several species of small mammal intermittently make use of the shelter or food provided by living commensally (e.g. Marsh & Harris 2000), only a very few can persist entirely in these habitats, and these have become some of the world's most cosmopolitan mammals and economically important species. They include house mice (Mus musculus domesticus Rutty) and rats (Rattus rattus L. and R. norvegicus Berkenhout). Commensal rodents demonstrate the adaptability of rodent lifestyles and their success in commensal habitats, wherever they have been transported by humans (Berry 1991).

The peculiarity of commensal habitats, compared to all other, non-commensal, habitats, requires small mammals to show particular lifestyle responses in order to persist. One distinctive feature of commensal habitats is that the environment is composed mostly of manufactured or human-arranged material, such as brick, concrete, transported rock or wood. Environmental conditions, such as temperature and humidity, are typically moderated compared with non-commensal habitats and they do not show such marked seasonal changes, but disturbance by humans or livestock can rapidly change these conditions. Similarly, food supply is less seasonally dependent and may be continuously superabundant, but the availability of food and shelter can rapidly change via human disturbance. Commensal habitats may also harbour high densities of predators, typically domesticated or semi-domesticated animals, maintained at artificially high densities by supplementary human feeding. All these features may affect the persistence of small mammal populations, both positively and negatively.

In this study we use house mice living on farms to test the effects of commensalism, comparing the study population with previous studies of house mice living in semi-natural habitats, so called ‘feral’ house mice (Berry 1991). On the mainland of Britain, house mice are almost entirely restricted to commensal habitats, where they cause damage to stored food products and may play a role in disease transmission to humans and livestock (Southern 1954; Berry 1991; Gratz 1994). Commensal house mice can occur at high densities (up to 7 m−2 in extreme cases; Berry 1991) and then populations are divided into discrete subgroups called demes, i.e. groups of related individuals in a territory defended by a dominant male (Gray, Jensen & Hurst 2000). In lower density commensal populations the territorial structure is present but more flexible (Crowcroft & Rowe 1963; Barnard, Hurst & Aldhous 1991).

House mice are excluded from field margins in many places, including mainland Britain, by competition from other small mammals, such as wood mice (Apodemus sylvaticus L.; Berry & Tricker, 1969; Tattersall, Smith & Nowell 1997). Feral house mice are only able to persist in open agricultural and natural habitats where there are no or few competitors. For this reason, they are found throughout Australia and New Zealand (e.g. Fitzgerald, Karl & Moller 1981; Singleton 1989), but in Europe and North America feral house mice are generally restricted to isolated islands (e.g. Lidicker 1966; Berry 1968; Triggs 1991; Berry et al. 1992).

Commensal house mice are frequently present and often abundant on farms (i.e. the farm yard, including houses, outbuildings, food stores and barns housing livestock) and so are most easily studied there (Rowe, Swinney & Quy 1983; Langton, Cowan & Meyer 2001). Farms represent islands of suitable habitat, well distributed across much of Britain (where our study was conducted), in a matrix of non-commensal habitat made unsuitable by the presence of other, competitively superior, small mammals. Within farms, though, suitable habitat is patchily distributed at the scale of a few metres (e.g. stores of animal feed localized in separate sections of barns), which is the scale of sub-structuring of house mouse populations. This spatial arrangement of food resources is different to that experienced by feral house mice, where seeds and other foods are distributed more widely in the landscape.

Demographic rates are often estimated in order to assess the responses of populations to differing environmental conditions (Lebreton et al. 1992). Specifically, the rates of births, deaths, immigration and emigration can be estimated from capture–mark–recapture (CMR) data. CMR models allow the estimation of population size (White et al. 1982), apparent survival (a function of mortality and permanent emigration; Lebreton et al. 1992), reproductive recruitment (Nichols & Pollock 1990), immigration (Nichols & Pollock 1990) and temporary emigration (Kendall, Nichols & Hines 1997). CMR studies also provide information about movement within the population, from the recaptures of individuals.

Our aim in this study was to test the responses of house mice to commensal habitats. We expected that they would have a high rate of reproduction to counteract a high population turnover, and flexibility in spatial organization in response to the spatial arrangement of food and shelter. To test this, we firstly estimated demographic rates of a commensal house mouse population living in a farm environment. Secondly, we used live-trapping to define the spatial subdivision of house mouse populations on two neighbouring farms. From this we could then record the frequency of movements out of subgroups to determine the degree of dispersal in commensal house mouse populations. These data could then be compared to published results of feral house mice.


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

study site and trapping regime

The study site was composed of the buildings and yards of two farms at Acaster Selby, North Yorkshire, UK (Ordnance Survey grid reference SE576407), which covered areas of 0·7 and 0·3 ha (Fig. 1). Both were mixed agriculture farms with seasonally present livestock and stores of grain, hay and straw. The farms were bounded by unsuitable, non-commensal habitats (grazing or arable land), although there were some thick hedgerows bordering the farms. The two farms were separated by 65 m, by a closely grazed field and a small plantation; the plantation had very little undergrowth and preliminary live-trapping revealed only the presence of wood mice and bank voles (Clethrionomys glareolus Schreber). The presence of sheep, grain or hay defined seasonal changes on the farm, which were: December to February (winter), March to May (spring), June to August (summer) and September to November (autumn). The intervals between capture sessions were assigned the season of the latter capture session. The study lasted for nine separate seasons, although the first (December 1998–February 1999) and the last (December 2000) were composed of only 2 months and 1 month, respectively. The main store, which was where most mice were caught, was cleaned and disturbed in February 1999 and May 2000.


Figure 1. Map of the study site obtained by field surveys. The study site was composed of two mixed agriculture farms at Acaster Selby, North Yorkshire, UK. The main features in the landscape, the farm houses and the buildings in which traps were set are all labelled. Hedges and plantations are shaded.

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House mice were studied by capture–mark–recapture using Longworth live traps (Penlon Limited, Abingdon, Oxfordshire; Gurnell & Flowerdew 1982) and individually marked with a uniquely numbered ear tag (100 Michel surgical staples, 11 × 2 mm in size, manufactured by Martin Medizintechnik, Tuttlingen, Germany; Le Boulengé-Nguyen & Boulengé 1986). The mice were weighed, sexed and their reproductive condition noted, before being released at the point of capture. Their age was defined as adult or juvenile based on external observation of reproductive maturity (young males had abdominal testes, young females were imperforate). This was closely related to mass, which is an indicator of age (Crowcroft & Rowe 1961); the average mass at which 50% of individuals captured were adult was about 11 g.

Our trapping regime used Pollock's robust design (Pollock et al. 1990), which involved two levels of trapping intensity. There were 25 monthly, primary capture sessions each with seven daily, secondary capture sessions. The aim was that each primary capture session (i.e. seven days) was so short that there was little or no birth, death, immigration or emigration, so the population could be treated as demographically closed and the population size estimated. Demographic rates such as survival and recruitment were estimated for the periods between primary capture sessions.

About 120 traps were used in each trapping session. A regular trapping grid could not be used in this study because the farm environment was heterogeneous both in time and space. Therefore traps were concentrated in areas where many mice were found, but arranged in suitable locations at lower densities elsewhere on the farm in a form of stratified sampling designed to maximize the number of captures while permitting all mice a chance of being caught (Greenwood 1996).

estimating population size

The population size was estimated for each primary capture session using the jackknife estimator in program capture (White et al. 1982; Rexstad & Burnham 1991). Prior expectation was that individual heterogeneity in capture probability was likely to be substantial within each primary capture session, due to effects of age, sex, social status or trap placement (Crowcroft & Jeffers 1961; Rowe 1970; Manning, Edge & Wolff 1995; Drickamer et al. 1999). The jackknife estimator accounts for this while being fairly robust to other sources of variation in capture probability (White et al. 1982) and violation of the assumption of closure (Kendall 1999). Prior selection of a default closed population estimator is useful when sample sizes are too small for model selection in capture (Menkens & Anderson 1988; Boulanger & Krebs 1994), such as in this study.

estimating survival rate

Survival was estimated as apparent survival rate (φ; a function of mortality and permanent emigration) using Cormack-Jolly Seber (CJS) models (Pollock et al. 1990). The confounding variable, recapture rate (p), was estimated simultaneously using maximum likelihoods in program mark (White & Burnham 1999). Several models were constructed with the parameters φ and p constrained to vary in biological plausible ways, as a function of time, individual (e.g. sex) or both (e.g. age-related effects).

Apparent survival was constrained according to sex (indicated in the model notation as g), season (s), population size at the previous capture session (ps; estimated with the jackknife estimator), effects of disturbance (d; lasting for 1 month after cleaning of the main store), age (a; adult or juvenile) or it was modelled as constant (·). Time variation in apparent survival was modelled to vary by season because the data were too sparse to model variation monthly. Individuals that were first recorded as juveniles were assumed to have become adult by the following primary capture session, since this was true for the majority (79%) of the 54 individuals first caught as juveniles and recaptured in the following primary session. Recapture rate was kept constant (·) or allowed to vary by season (s) or capture history (r; comparing first and subsequent captures).

A set of 18 candidate models was constructed (15 are shown in Table 1), including only models that were biologically feasible and according to a priori hypotheses about the factors affecting survival and recapture rate, explained above (Burnham & Anderson 1998). Only simple candidate models, with no interactions, were constructed because increasing the complexity of the models required data of higher quality than were available.

Table 1.  Results of the model selection. Apparent survival (φ) and recapture rate (p) vary according to sex (g), age (a), population size (ps), season (s), recapture history (r) or are constant (·). AICc is a measure of the parsimony of each model. Δi is the difference in AICc between the best and current model. The Akaike weight is the normalized likelihood of each model. The number of estimable parameters (K) and the deviance of each model are also shown. The best approximating models are shown in bold
ModelAICcΔiAkaike weightKDeviance
{φ(·)p(s)}1331·06 0·000·39210630·25
{φ(g)p(s)}1331·94 0·870·25311629·06
{φ(ps)p(s)}1332·98 1·920·15011630·11
{φ(a)p(s)}1333·05 1·990·14511630·17
{φ(s)p(s)}1334·85 3·790·05917619·51
{φ(·)p(r)}1350·8519·790·000 3664·29
{φ(·)p(·)}1351·0519·990·000 2666·51
{φ(ps)p(r)}1351·2820·220·000 4662·70
{φ(ps)p(·)}1351·4220·360·000 3664·86
{φ(g)p(r)}1352·0220·960·000 4663·44
{φ(g)p(·)}1352·2121·140·000 3665·65
{φ(a)p(·)}1352·5121·450·000 3665·95
{φ(a)p(r)}1352·7021·630·000 4664·11

We used program u-care to test for goodness-of-fit to CJS models (Choquet et al. 2003). There was no evidence for lack of fit (overall: inline image = 83·72, P = 0·206; males: inline image = 48·58, P = 0·647; females: inline image = 33·47, P = 0·995). Although these non-significant results did not necessarily validate the use of CJS models (since non-significance could be observed through lack of data), they did not provide any evidence for lack of fit.

The fit of the models to the data was estimated using an information theoretic approach with program mark (White & Burnham 1999). Using this approach, models were ranked according to AIC (Akaike's Information Criterion), a relative measure of parsimony, i.e. the balance between number of parameters and the fit of the model (Burnham & Anderson 1998).

estimating recruitment from in situ reproduction and immigration

Using the robust design it was possible to estimate recruitment and its components, namely, in situ reproduction and immigration (Pollock et al. 1990). Because it was assumed that there was individual heterogeneity in recapture probability (hence the use of the jackknife estimator), maximum likelihood methods could not be used. Instead we used ad hoc estimators for recruitment and its components (Nichols & Pollock 1990; Pollock et al. 1990). Total recruitment (Bi) refers to the estimated number of individuals joining the population between time i and i+ 1. The individual components are estimated for recruitment to the adult population. Recruitment to the adult population via reproduction at time i+ 1 is a function of the estimated number of juveniles at time i and their survival rate between i and i+ 1, given that juveniles mature in the time interval between primary capture sessions (Nichols & Pollock 1990). The expected number of adults at time i+ 1 was calculated from the number of adults expected to have survived from time i and the estimated number of recruits via reproduction. The difference between this and the actual estimated population size gives the estimated number of immigrants into the adult population during the interval. Nichols & Pollock (1990) and Pollock et al. (1990) provide full details of the estimation of these components and their variances. Temporary emigration can also be estimated from CMR data (Kendall et al. 1997), but it requires larger sample sizes than were obtained in this study, so was not estimated.

spatial arrangement of the population

The spatial arrangement of vertebrate populations can be based on observing social interactions to define subgroups in the population (e.g. Crowcroft & Rowe 1963) or habitat distinctions, which are assumed to have an effect on the population (e.g. Peles, Bowne & Barrett 1999). This study used an objective method to subdivide the population without needing to observe social interactions or rely on apparent, sometimes arbitrary, habitat distinctions. Spatial analysis of capture data is currently in development and fully objective statistical methods are only suitable in specific circumstances (Borchers, Buckland & Zucchini 2002), so we used the semi-automated method based on a Geographic Information System (GIS) described by Pocock et al. (2003). With this process, a grid of points was laid over a map of the field site and trap locations within a GIS and the ‘accessibility’ of each grid point to every trap was calculated. Accessibility was used as an interpolation tool based on the weight of each trap location subject to an appropriate distance decay function, summed for all trap locations. It is analogous in its calculation to ‘connectivity’ used in metapopulation studies (Hanski, Alho & Moilanen 2000). The distance decay function was based on a power curve (y = ax−β) where the distance decay coefficient, β = 1·418, was estimated from the distances between recaptures for mice in this study (Pocock et al. 2003). The resulting surface represented a function of the predicted number of mice and was divided into subgroups using morphometric feature analysis (Wood 1996). This distinguished between regional maxima in the surface and divided the trap locations into subgroups each centred on a peak in this surface. Individuals were assigned a subgroup based on the trap in which they were captured.

Some adjustments were made based on comparisons between prior and subsequent primary capture sessions and knowledge of the farm layout, but even so, the method was more objective than using ‘expert knowledge’ or habitat distinctions alone. Specifically, a small subgroup (containing one or two trap locations) was combined with its neighbour if the two were united in the prior or subsequent month or if it was in a discrete area (e.g. a barn) with its neighbour and isolated from other subgroups. A subgroup was split into two if it encompassed an area that on previous and subsequent occasions was defined as two subgroups or there was an impermeable barrier clearly bisecting the subgroup.

Once subgroups were defined it was possible to use movement away from a subgroup as the best measure of dispersal, when defined as movement away from a home range (Stenseth & Lidicker 1992a). This was compared to long distance movement, sometimes used as a surrogate for dispersal (e.g. Rowe, Quy & Swinney 1987), using two arbitrary cut-off distances (10 m and 30 m). All movements were categorized on the basis of the sex and the age of the individual. Differences in the frequency of dispersal between different ages or sexes were tested using a goodness-of-fit (G) test.


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

population size

During the study there were 1620 captures, with a total of 568 individual house mice being caught. There were 1053 recaptures of individuals and the median number of captures was two per individual. Double marking individuals by ear tagging and fur-clipping showed that ear tags were lost in only 3·6% of recaptures.

Population size appeared unaffected by seasonal changes in the use of buildings on the farm or disturbance, when the main store was cleaned (Fig. 2). The lower population size in the second year of study was probably due to a reduced amount of cover, although this was not measured.


Figure 2. The total number of house mice caught during each primary capture session on two adjacent farms at Acaster Selby, North Yorkshire, UK. The population size at each session was estimated using the jackknife estimator in program capture. The main store was the only building where house mice were always captured and the occasions when it was cleaned are indicated with arrows.

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survival rate

The three models, in the candidate set of 18, that included the effects of major disturbance (i.e. the aseasonal effects of cleaning the main store) were excluded from further consideration because the paucity of the data resulted in biologically implausible results (survival rates higher after disturbance than before). The ranks of the remaining 15 models (Table 1) showed that four had substantial support as the best approximating model (assessed as Δi < 2; Burnham & Anderson 1998) and the same four models comprised the 90% confidence set (based on the Akaike weights; Buckland, Burnham & August 1997). With similar examples, Burnham & Anderson (1998) suggest that the simplest of the best approximating models should be used with unconditional variances estimated from the candidate model set. Therefore, the best model was {φ(·) p(s)} and the monthly rate of apparent survival was estimated to be 0·539. There is little evidence that there was a trap response, and although the recapture rates vary over time (Table 2), the variation could not be related to seasonal changes in activity on the farm.

Table 2.  Estimated recapture rates from the top-ranked model {φ(·)p(s)}. The seasons are defined in the text
SeasonYearRecapture rateSE

The unconditional variance of apparent survival for each group within the population (adult and juvenile male, adult and juvenile female) was estimated for each interval (Buckland et al. 1997), but there is no formal methodology for combining variances of groups in such situations (i.e. combining the unconditional variances of the four groups into a single value). We decided to be conservative and select the largest of the four variances for each time interval as a measure of unconditional variance (Table 3). Changes in unconditional variance did not show clear trends over the course of the study, but it was highest when population size was smallest. The 95% confidence intervals around the estimated survival rate (0·539) were 0·453 and 0·625 (based on the maximum unconditional standard errors).

Table 3.  Unconditional standard errors (unc. SE) from model averaging with the candidate model set. The best estimate of apparent survival was 0·539
IntervalStart monthUnc. SE
  • *

    There is no estimate from the final interval because in some models it could not be estimated due to the paucity of the data.

1December 19980·0387
2January 19990·0388
3February 19990·0363
4March 19990·0368
5April 19990·0365
6May 19990·0401
7June 19990·0348
8July 19990·0353
9August 19990·0418
10September 19990·0377
11October 19990·0379
12November 19990·0436
13December 19990·0413
14January 20000·0422
15February 20000·0393
16March 20000·0368
17April 20000·0375
18May 20000·0404
19June 20000·0437
20July 20000·0426
21August 20000·0354
22September 20000·0345
23October 20000·0339
24November 2000*

recruitment by in situ reproduction and immigration

The estimated total number of recruits to the population varied over time (Fig. 3), but was greater than zero for most intervals.


Figure 3. Estimates of total recruitment over the whole study site for each interval between primary capture sessions, with the 95% confidence intervals derived from the unconditional SE. Each interval was one month long and started in the month indicated. The horizontal line indicates zero recruits. Note that there is no variance for the last interval since the unconditional SE could not be estimated for this period.

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The components of recruitment were modelled using apparent survival of adults and juveniles and their variance and covariance, estimated with the model {φ(a)p(s)}. This model had substantial support as the best approximating model (Δi = 1·99) and was the best supported model with age-structured variation. The apparent survival for juveniles was 0·524 (SE = 0·059) and for adults was 0·541 (SE = 0·024); the covariance of the two was 4·28 × 10−5. The reported standard errors were conditional on this model, i.e. less than the true (unconditional) standard errors, but the results would be changed little unless the standard error was substantially increased. The number of juveniles captured was lower than adults and only sufficiently high (> 10) for their numbers to be estimated with the jackknife estimator during the first half of the study. Therefore, the components of recruitment are shown for intervals 1–13 only. For this period the estimated number of juveniles recruited to the adult population was always significantly greater than zero (Fig. 4), suggesting that breeding occurred throughout the year. Reproductively active females (visibly pregnant or lactating) and juveniles were caught in every primary capture session during the study, confirming this conclusion. The estimated number of immigrants was significantly greater than zero only during two intervals in the first half of the study (Fig. 4).


Figure 4. Estimates of recruitment to the adult population for the whole study site, separately estimated as recruitment via (a) in situ reproduction and (b) immigration for each interval between primary capture sessions. The bars show the 95% confidence intervals based on the conditional SE from the model {φ(a)p(s)}.

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subgroups and dispersal

In total, 176 subgroups were defined, after the modifications described in the Methods, 17·0% of which were the result of clumping and 5·1% the result of splitting subgroups. The majority of subgroups therefore remained unaffected by these alterations. The subgroups for each capture session were associated on the basis of their location to give 13 subgroups (Fig. 5). Not every subgroup was present during each capture session (due to movements of individuals or local extinction) and the size and extent of each varied over time.


Figure 5. The maximum area of each of the 13 subgroups identified over the course of the study. The subgroups were identified as described in the text. They did not overlap within each primary capture session (although the maximum extents do overlap) and not all subgroups were defined in every month.

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There were relatively few movements between subgroups (only 70 of 1053 recaptures; Fig. 6). Only three of these movements were between the farms (i.e. > 100 m) and all were from the larger to the smaller farm. There was a significant bias in male dispersal, as defined as movements away from a home range, but this was not significant when only long-range movement of either 10 or 30 m were considered (Table 4).


Figure 6. The frequency of movements between recaptures of individuals between subgroups (dispersal) and movements within subgroups (non-dispersal) according to sex. Note that the frequency is a logarithmic scale.

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Table 4.  Numbers of long-distance and dispersal movements for different ages and sexes. Note that recaptures in the same trap (i.e. movements of 0 m) are included in the results. Dispersal movements are those where an animal moved between subgroups. Where ages differed between two captures the animal was placed in the class for its age at first capture
  JuvenileAdultG (d.f. = 3)P
Dispersal movementYes 16  9 30 159·930·019
Recapture distance> 10 m 18 15 45 245·790·122
< 10 m 98144413296  
Recapture distance> 30 m  7  6  8  76·420·093
< 30 m109153450313  


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

the importance of births and deaths

The estimate of apparent survival (0·54 per month) equates to an annual survival rate of 0·0006. Other reported monthly rates of apparent survival in commensal house mice are 0·3 (Singleton 1983) and 0·5–0·7 (Rowe et al. 1987), compared to 0·7–0·9 for feral house mice (Berry 1968; Fitzgerald et al. 1981; Triggs 1991). The calculation of these published figures did not take account of recapture rate so they underestimate apparent survival. However, the results suggest that commensal house mice have lower rates of apparent survival than feral house mice. Although survival rate shows variation over time in commensal house mice, neither our data nor published data show a seasonal or other systematic trend (Singleton 1983; Rowe et al. 1987). In feral house mice, however, survival appears to vary seasonally according to food availability and climatic conditions (Stickel 1979; Berry & Jakobson 1975); the specific pattern varies according to the individual circumstances. Changes in survival rate also appear to be one of the factors influencing plague formation (Singleton 1989).

The population persisted throughout this study, despite such low apparent survival, so there must have been a rapid turnover of individuals in the population and recruitment rates must have been high. Changes in the rate of recruitment appeared to have a greater effect on population size than changes in apparent survival, because population size changed markedly, but apparent survival was best modelled as constant. Recruitment is also more important for feral house mice, because the failure of recruitment through reproduction, rather than changes in survival rate, was the cause of decline and ultimate extinction in two island populations (Lidicker 1966; Berry & Tricker 1969; Berry, Cuthbert & Peters 1982).

The estimates of the two components of recruitment showed that recruitment by in situ reproduction occurred throughout the year and was numerically more important than immigration (Fig. 4). The fecundity of house mice is certainly sufficient to allow the population to persist even with low survival rates (Berry 1981). Unusually among small mammals, reproduction in house mice is not controlled by photoperiod but females stop breeding when the temperature is low and food is scarce (Bronson 1979; Pelikán 1981; Perrigo & Bronson 1985). Because house mice in this study bred throughout the year, there must have been sufficient food during the colder months to allow continuous breeding, which is not true for house mice in non-commensal habitats in Britain (Berry 1968; Triggs 1991).

The peaks in immigration (Fig. 4) suggest that it is occasionally substantial. Immigrants could have come from nearby commensal sites (i.e. neighbouring farms), but natural long-distance movement is too unusual to explain the peaks in immigration (Pocock et al. 2003). They could have arrived as stowaways in deliveries from other commensal sites (Baker 1994), but there were no observation of incoming mice and no major deliveries when the peaks occurred. It is also unlikely that large numbers of house mice moved from non-commensal habitats, because the peaks in immigrants were not related to seasonal changes in the environment, such as harvest, as they are elsewhere (Carlsen 1983) and house mice are rarely found in field margins at any time of the year in Britain. The most likely alternative is that immigrants to the trappable population came from untrappable areas within the study site, such as animal pens or the inside of haystacks, where disturbance and lack of access prevented traps being placed. The trappable population was therefore less than the total population size. House mice were observed moving from a previously untrappable animal shed as it was being cleaned in May 1999 (i.e. during the first peak in the number of immigrants), but there is no clear explanation for the second peak in the number of immigrants. Other studies of non-commensal rodents that have estimated components of recruitment have shown that reproduction and immigration are important at different times of the year (Nichols & Pollock 1990; Paradis 1995; Lima & Jaksie 1999), so unlike commensal house mice, these species show seasonality in breeding and dispersal.

Although both mortality and permanent emigration influence apparent survival, movement was limited in our house mouse population (Pocock et al. 2003), so the low rates of apparent survival are probably due to high mortality. The poor support for model {φ(ps) p(r)} suggests that density dependence in survival is not strong. Of the factors listed by Berry, Jakobson & Triggs (1973), predation (and poisoning) is probably the most important factor in survival rate in this study. The main predators are probably cats, rats and chickens (although it seems unlikely, a cockerel was observed to catch a mouse during the study). Changes in the amount of cover may have influenced the opportunity for predators to encounter house mice. Some potential pathogens were monitored (Pocock et al. 2001), but they were at low prevalence and no external evidence of disease was found. This is clearly different from feral populations, in which environmental stress (food supply and climate) is probably most important, coupled with disease in the case of plague populations (reviewed by Berry et al. 1973).

dispersal as a response to habitat characteristics

The evidence from demographic parameters was that movement in to or out of the population was not substantial, and the population dynamics were driven mainly by births and deaths. The prior expectation was that the population would be composed of demes that were geographically isolated and had relatively little movement between them (Gray et al. 2000). This was confirmed in part by the small number of long distance movements that were recorded in this population (Pocock et al. 2003) and justifies the use of morphometric feature analysis based on measures of accessibility to define subgroups in the population. Based on the definition of subgroups, dispersal was an uncommon event but it operated over relatively short distances (mostly less than 30 m) and was male biased (Fig. 6), as predicted by the system of male territoriality (Brandt 1992). This confirms the results of other studies of commensal house mice (Singleton 1983; Rowe et al. 1987). The scale of movements made by commensal house mice, both distance and frequency, is considerably less than non-commensal house mice (Berry 1968; Newsome 1969; Stickel 1979; Singleton & Redhead 1990) or other small mammals (Krohne & Burgin 1990; Steen, Ims & Sonerud 1996; Bowman, Forbes & Dilworth 2000). The lack of movement in the population can be explained by the habitat in which commensal house mice live. Low rates of dispersal are favoured under conditions of resource decentralization, e.g. food stores located throughout buildings (because it reduces territorial aggression, a cause of dispersal in male house mice; Maly, Knuth & Barrett 1985), and the aggregation of suitable patches of habitat, e.g. barns grouped in farms (Johst, Brandl & Eber 2002). In contrast, Stenseth & Lidicker (1992b) predict that dispersal (specifically, presaturation dispersal) should be pronounced in r-selecting conditions, characterized by small, relatively ephemeral patches of suitable habitat surrounded by a matrix that is not too hostile. It may be that the non-commensal matrix is too hostile and suitable habitat patches too far apart for this to influence strongly the spatial dynamics of commensal house mice. For feral house mice, food sources are scattered more widely over the landscape, thus favouring long distance movements and hierarchical rather than territorial systems.

Although the observed rates of dispersal are lower for commensal than feral house mice, commensal mice are able to move long distances between habitat patches with humans, as stowaways (Baker 1994). Once in suitable habitat, their rapid reproductive rate allows them to establish viable populations from a single pregnant female or pair of mice and by breeding throughout the year they are able to persist in habitats unsuitable for other small mammals. However, the flexibility of house-mouse life histories allows them to persist successfully in both commensal and non-commensal habitats across the world.


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We are very grateful to the farmers, Mr & Mrs Dean and Mr & Mrs Rowlay, who gave permission for this study to be conducted on their farms. M.J.O.P. was supported by a studentship from Humberside Wastewise.


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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