• perturbation;
  • population structure;
  • wildlife disease;
  • zoonosis


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • 1
    The culling of European badgers Meles meles has been a central part of attempts to control bovine tuberculosis (TB) in British cattle for many years. Recent results, however, indicate that this approach could in practice enhance disease spread.
  • 2
    This paper looks at the relationship between TB incidence and badger ecology in a high-density population in south-west England, which has been the subject of a long-term intensive study. The principal aims were to relate the probability of TB incidence, as detected by culture of clinical samples (i.e. excretion of bacilli), at the level of the individual and of the social group to demographic processes, movement, social organization and disease dynamics.
  • 3
    The probability of an individual being an incident case was greater in groups where TB was already present, although this was less influential in groups that were subject to some instability in numbers. Both individuals and groups were more likely to be incident cases where the social group was diminishing in size, although no relationship was observed with group size itself. This suggests that the process of group size reduction rather than group size per se has most influence on disease dynamics. The likelihood that either an individual or a group was an incident case was positively correlated with both individual and group-level movement. When the proportion of females in a social group was high, the positive association between movement and incidence was found to be more pronounced and there was a significantly higher probability of incident cases among males.
  • 4
    These relationships highlight the importance of social structure in driving TB transmission dynamics in this stable, high-density badger population. The results support the idea that a stable social structure mitigates against new incident cases of disease, and are consistent with the contention that badger culling may create the social circumstances for enhanced transmission of TB.


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

Determining the factors that influence the transmission and persistence of infectious diseases in free-living wildlife is a central question in population ecology, with important implications for disease management. The incidence of infectious disease is defined as the number of new infections out of a total of all susceptibles per unit time. Although this is expected to vary with population size, other factors may also limit host interactions and contact rates (Loehle 1995; Lloyd-Smith et al. 2005). For example, the spatial or social structure of a population may influence the rate of disease spread (Keeling 1999) and disease persistence (Keeling 2000; Hagenaars, Donnelly & Ferguson 2004). Nevertheless, few empirical studies have described the incidence of infectious diseases in socially structured wildlife populations. Such information is, however, crucial to understanding how population structure impacts on disease transmission (Anderson & May 1991) and the implications for management.

Bovine tuberculosis (TB) is a chronic infectious disease caused by the bacterium Mycobacterium bovis and affects a broad range of mammalian hosts (De Lisle, Mackintosh & Bengis 2001). Its re-emergence and the repeated failure to eradicate TB from livestock in several countries have been related to reservoirs of infection in wildlife populations. In Britain, data suggest that bovine TB in both European badgers Meles meles (Lin. 1758) and cattle is a low incidence infectious disease with an apparently relatively low, but variable, transmission rate (Smith et al. 2001; Cox et al. 2005). In the UK, the badger is widely considered to represent a significant wildlife reservoir for the transmission of M. bovis to cattle (reviewed in Krebs 1997). Since 1973, a variety of badger culling strategies have been used by the UK government as the principal tool to reduce risks of transmission to cattle (Krebs 1997). Despite this, however, the number of cattle compulsorily slaughtered owing to TB has continued to rise (Gilbert et al. 2005) and evidence from the UK Randomized Badger Culling Trial (RBCT) showed that localized reactive culling failed to control TB incidence in cattle (Donnelly et al. 2003). The RBCT also showed that, although proactively culling badgers over larger areas did reduce TB incidence in cattle in the culled areas, it increased in adjoining areas (Donnelly et al. 2006). Complementary field studies of badger territoriality suggested that badger culling disrupted the otherwise stable territorial system and could potentially increase rates of contact among badgers and between badgers and cattle (Woodroffe et al. 2005), a phenomenon that could have contributed to the results of the trial.

The possibility that badger culling could have counter-productive effects on disease control was not a new idea (Cheeseman et al. 1993; Smith et al. 1995; White & Harris 1995; Neal & Cheeseman 1996; Rogers et al. 1998; Tuyttens et al. 2000a,b). Previous field studies have shown that culled badger populations experienced extensive social perturbation, including enhanced movement (e.g. Cheeseman et al. 1993; Tuyttens et al. 2000a). As a consequence the implicit assumption that disease transmission diminishes proportionally with reductions in host density (Anderson & May 1979; May & Anderson 1979; Wobeser 1994) may not be valid in this case. Instead, the transmission of M. bovis in badger populations may be profoundly influenced by the prevailing social structure.

At medium to high densities, badger populations throughout most of Britain tend to exhibit a high level of social organization. In such circumstances badgers typically live in social groups of three to 10 individuals and mutually defend a group territory (Neal & Cheeseman 1996). These are mainly composed of individuals that stay in their natal group (Kruuk & Parish 1982; Cheeseman et al. 1987) as dispersal rates are low (Woodroffe, Macdonald & Da Silva 1993; Cheeseman et al. 1988a; Rogers et al. 1998). The majority of dispersal movements occur between adjacent groups (Rogers et al. 1998), although transient excursions may be associated with breeding behaviour (Woodroffe et al. 1993) and inbreeding avoidance (Carpenter et al. 2005). Evidence from the intensive long-term study of an undisturbed high-density badger population at Woodchester Park (WP), Gloucestershire, suggests that the organization of the population into social groups limits the spread of TB (Delahay et al. 2000b), and that increases in the movement of individuals between groups are followed by a rise in the incidence of disease (Rogers et al. 1998).

Further investigation of the factors that influence TB incidence in badger populations is clearly important in informing the development of sustainable approaches to managing risks to cattle. In particular, given the potential for demographic perturbation inherent in management intervention, understanding the role of intergroup movements in the spread of infection is of vital importance. The present paper is based on results of demographic and epidemiological data from the long-term study of the WP badger population and examines relationships between TB incidence and aspects of population structure and movement, at the level of the individual and social group, respectively.


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

study area

The data were collected from the WP study area, located on the Cotswold limestone escarpment of South-west England. The resident badger population included an intensively studied core area of approximately 7 km2 of mixed woodland, grassland and arable (see Delahay et al. 2006). Central to the study area was a steep-sided wooded valley, surrounded by agricultural grasslands, principally for beef and dairy cattle grazing. Main setts were relatively regularly spaced throughout the study area, being largely located along an exposed band of Cotswold sandstone.

badger trapping and sampling

Data for the present study constituted trapping records for intensively studied social groups at the centre of the study area (hereafter referred to as the ‘core groups’) from 1990 to 2004 inclusive (data from 1989 were used to calculate trends and characterize movement in 1990). Each social group was trapped four times per year, using steel mesh box traps baited with peanuts and set after 4–8 days of pre-baiting. Traps were located at the active setts in each territory. Trapped badgers were anaesthetized with ketamine hydrochloride (Vetalar™ V, Pharmacia and Upjohn, Crawley, UK) alone (MacKintosh et al. 1976) until 2001, and in combination with medetomidine hydrochloride (Domitor®, Pfizer, Sandwich, UK) and butorphanol tartrate (Torbugesic®, Fort Dodge Animal Health Ltd, Southampton, UK) thereafter (De Leeuw et al. 2004). On first capture each badger was given a unique identifying tattoo on the belly (Cheeseman & Harris 1982). For each capture event, the location, sex, body weight, body condition (scored between 1 and 5 based on fat coverage and musculature of shoulder blades, pelvic region, ribs and vertebrae), tooth wear [five categories from none (0) to worn flat (1)] and (for animals first caught as cubs) the age class (< 1 year = cub, 1 year  adult) were recorded. Samples of faeces (by application of an enema), urine (by external palpation of the bladder), sputum (by aspiration of the oesophagus or trachea), pus and exudate from abscesses and bite wounds (by swab or needle biopsy) were taken from anaesthetized badgers (Clifton-Hadley, Wilesmith & Stuart 1993) and subjected to bacterial culture for M. bovis (Gallagher & Horwill 1977). A sample of jugular blood was taken by venepuncture and subjected to an enzyme-linked immunosorbent assay (ELISA) (Goodger et al. 1994) for the detection of antibodies to M. bovis. After a period of recovery all trapped badgers were released at the point of capture. All badger carcasses found in the core area throughout the study were submitted for post-mortem examination for tuberculous lesions, and tissue samples from lymph nodes and major organs were taken for bacterial culture. These animals and their associated culture results were treated in the same way as live cases in the estimation of both individual and group-level parameters.

The culture of clinical samples only detects animals that have established disease and are excreting bacilli (Pritchard et al. 1986). These animals may exhibit intermittent excretion, and continuous excretion would only be expected in cases of advanced disease (see Gallagher & Clifton-Hadley 2000). Latency is a feature of many TB infections but these can be triggered to develop to florid disease with excretion, as a consequence of external stressors, and complete ‘self-cure’ is likely to be rare (Gallagher et al. 1998). Latency can last for decades in humans (Sjogren & Sutherland 1975) and can probably be measured in years in the badger (Gallagher et al. 1998). Complete resolution of infection is most likely to occur in individuals following containment at an early stage of pathogenesis of disease. The culture of clinical samples, however, is unlikely to identify animals in the early stages of disease when excretion of bacilli may be absent or scant and extremely difficult to detect (see Gallagher & Clifton-Hadley 2000) and so is unlikely to identify animals in which infection is subsequently completely resolved. A culture-positive result can therefore be taken as evidence of established disease, with the likelihood that the individual will remain potentially infectious thereafter. Consequently, in the present study, animals that were positive on the culture of at least one clinical sample in a particular year were categorized as ‘excretors’ from that year onwards. An animal that had not returned a single positive culture result prior to a given year was considered a susceptible case for analytical purposes in said year. Similarly, where post-mortem examination was carried out, any badger from which a positive tissue culture result was obtained was classified as an ‘excretor’ for that year only if it had not tested positive before. A culture-negative result at post-mortem examination resulted in the animal being classified as a ‘susceptible’ case (i.e. previously culture negative) for that year. The serological ELISA has high specificity but low sensitivity (Goodger et al. 1994) although the latter is increased for badgers with progressive TB (Clifton-Hadley, Sayers & Stock 1995). In addition, a positive ELISA result is correlated with an enhanced likelihood of a future culture-positive result (Chambers et al. 2002). As the presence of antibodies in an individual could correlate with inactive (latent) or active infection, or potentially with immunity (Newell, Clifton-Hadley & Cheeseman 1997), ELISA positive results were considered only as evidence of exposure. Taking into account these diagnostic constraints, we assessed the influence of social organization and movement on the incidence of M. bovis excretion, and only used ELISA results to speculate on subsequent predictions.

social group delineation

The configuration of badger social groups was determined in the spring of each year by bait marking (Delahay et al. 2000a), which involved deploying a palatable bait of peanuts and syrup laced with small coloured plastic pellets for 10 days at the main sett of each social group. The harmless indigestible pellets were passed in the faeces of badgers that consumed the bait and subsequently identified in droppings at latrines. By using pellets of a different colour and shape at each main sett, droppings could be allocated to particular social groups. Boundaries between adjacent groups were characterized by the presence of latrines with droppings containing pellets fed at both. The distribution of plastic pellets at latrines and field records of boundary runs were used to digitize social group territories using a geographical information system (ArcView 3·2, ESRI 1996).

data analysis

Badgers were usually trapped more than once, resulting in repeated observations of most individuals in each year. Before analysis each captured badger was allocated to one social group and disease status category (see above) in each calendar year. Criteria for the allocation of badgers to a social group were, in order of priority: (1) allocate to the group where most frequently caught in that year; (2) refer to allocation(s) in adjacent years and allocate to the group where most frequently caught; (3) consider the number of captures in current and both adjacent years and allocate to the group where most frequently trapped; (4) consider the badger's last capture before the year in question and the first capture after that year. In both cases, calculate the time lag to/from the first/last date of the year in question and allocate to the group for which this time period is the shortest (assuming this is one of the most frequent groups as defined in step 3); (5) allocate to the first relevant group caught in if otherwise indeterminable. Over 96% of individuals were allocated to a social group on the basis of criterion 1.

We explored correlations between TB excretion incidence and various potential explanatory variables, based on demographic and movement histories using a series of models at the (1) individual, and (2) social group level. All analyses were carried out as generalized linear mixed models (GLMMs) in SAS (Glimmix Procedure; SAS version 9·1·3. SAS Institute Inc., Cary, NC, USA). The binary response variables were modelled with a binomial distribution and analysis carried out on the logit transform. Social group and year (1990–2004) were incorporated as random effects in the individual-level analysis. In all analyses, two-way interactions, except those including the number of captures (see below), were included in an initial model and backward elimination of nonsignificant terms using a threshold of 5% was used to identify the minimum adequate model (Crawley 1993). Akaike's criterion was used to assess the fit of the model (Burnham & Anderson 1992). All statistics are shown with 95% confidence intervals. We used 33rd and 66th percentiles of group terms for post-analysis comparisons.

In individual-level analyses, only badgers that were first caught as cubs could be accurately aged in years. However, as toothwear scores were highly correlated with age in years in a subsample of badgers of known age (Spearman correlation, RS: 0·811, n = 2092, P < 0·001), the annual mean toothwear score for each individual was used as a proxy for age in individual-level analyses. Each individual was also allocated to a movement category for each year based on whether it had moved since the previous year. Individuals allocated to the same social group in both years were ‘non-movers’, and those that had moved between two different groups in the core area were ‘core movers’. Animals that had either been captured previously in a group outside the core area or were adults when first caught in the core area were classed as ‘core immigrants’. Consequently, with the exception of animals first caught as adults in the core area, the cases included in the analyses refer to individuals captured at some point in both the current and previous year. Non-immigrant animals that had not been captured the previous year were excluded from the analysis due to the higher level of uncertainty regarding the timing of excretion incidence. As there was no clinical information for animals first caught as adults and assumed to be ‘core immigrants’ for the year before they were captured in the core area, we assigned them negative status if they tested negative to M. bovis excretion on first capture.

For both individual- and group-level analyses, social group characteristics were included as independent variables. Group size for a given year was estimated using the minimum number alive method (MNA, Rogers et al. 1997). Although this will tend to underestimate group size, it constitutes a standardized approach and bias would be expected to be consistent throughout the study period (Rogers et al. 1998). For each year the prevailing trend in the size of each group was calculated as the difference in estimated group size between the current and previous year expressed as a percentage of the previous year's value (hence, a negative value described a group of decreasing size). Group sex ratio was calculated as the percentage of adult females in the group in relation to the total number of adults. Annual group TB prevalence was the percentage of resident badgers that were identified as having excreted M. bovis at some point in their lives. For each observation, this value excluded the dependent case (individual in a given year). For all subsequent years after an animal was an incident case, it was classified as a prevalent case and therefore contributed to the calculation of group TB status. An additional term, the ‘group movement index’, based on movements of individuals within a group was also included in the analyses to quantify badger movements at the group level. For this purpose, all relevant badgers were assigned an annual intergroup movement mean score [derived from individual scores allocated at each trap event, according to whether there had been a change in group since the previous capture (score = 1) or not (score = 0)]. The ‘group movement index’ was calculated from the average of all movement scores of individuals within a group. For analysis, individuals were assigned the group movement index of their resident group in the previous year. As cubs rarely move (Rogers et al. 1998) but the proportion in a group would substantially affect the movement index, and as they are infrequently TB positive (Delahay et al. 2000a), observations on cubs were not included in the calculation of this index. We selected the group movement index of the group the animal was resident in the previous year since exploratory analysis indicated that this was more highly correlated with the probability of incidence than concurrent group-level movement.

individual-level analyses

Behavioural and demographic correlates of the incidence of TB excretion in individuals were analysed by fitting a GLMM. Individual TB excretion incidence status (0 = negative, i.e. all culture results negative, 1 = incident excretor case, i.e. ≥ 1 culture results positive) was then used as the dependent variable, with sex, age (as measured by toothwear) and individual movement (see above) as independent individual-level explanatory variables. In addition, the following group-level independent variables were included: sex ratio, group size, the prevailing trend in group size, the movement index and TB prevalence. The number of times an individual was captured and tested in a given year (as a log10 transformation) was also included in the model (but not its interactions with other factors) to control for the possibility that incidence was more likely to be detected in animals that were caught more often.

Correlates of movement were considered in the context of the relationship between incidence and movement at the individual level. There is some anecdotal evidence that individuals in poor body condition, may be more likely to move (e.g. Muirhead, Gallagher & Burn 1974; Cheeseman & Mallinson 1981) and there is robust evidence of a correlation between TB excretion status and ranging behaviour (Garnett, Delahay & Roper 2005). It may also be hypothesized that existing but presumed latent infection (seropositive but still negative excretor individuals) could also potentially influence movement. Hence, the possible influences of body condition and ‘exposure’ status were considered in a GLMM analysis with the individual probability of movement as the response variable. Serological status in the previous year was expressed as a binomial variable (0 = all ELISA tests negative, 1 = at least 1 ELISA test positive) and body condition expressed as an annual mean of the individual scores. The effects of sex and group size were also included in the model to adjust for any related bias, and social group and year were included as random factors.

social group-level analyses

Three GLMMs were carried out to identify correlates of group-level TB incidence. In the first model group-level incidence was described as a binary response. Analysis was restricted to susceptible groups (i.e. those with no prevalent cases resident in year t − 1). A social group was classified as an incident case (status = 1) if at least one individual TB-positive case was detected in it during that year and none had occurred in the previous year, and ‘negative’ (status = 0) otherwise (n = 242 observations). When TB was present in the group for at least two consecutive years, the group was classified as a prevalent case (i.e. at least one positive culture had occurred in the previous and the present year). A second model compared incident groups with all non-incident groups (i.e. negative groups and prevalent but nonincident groups). In the third model group-level incidence was defined as the number of incident cases as a proportion of the number of susceptible cases within the group.

In all analyses, backwards-stepwise regression was used to identify the final model as described previously. In the first two models the following group-level variables were included: sex ratio, group size, the prevailing trend in group size, movement index, the number of immigrants from a core group, and the number of immigrants from a non-core group. The same explanatory variables were used in the third model, but in addition, a term for the present annual group TB prevalence (excluding incident cases) was included.

Finally, a logistic regression was carried out to assess the effect of group-level TB prevalence on probability of movement while controlling for group size. Inference on paired prevalence comparisons was made using Chi-squared tests.


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

During the period 1989–2004 the number of badger social groups identified in the core of the study area each year by bait-marking remained relatively constant (mean = 25, range = 23–27, Fig. 1a). However, despite the identification by bait-marking of a group range, no resident badgers were captured in one group in 1993, 2002 and 2003. As the number of social groups remained relatively constant, variations in population size during the study (the overall number of badgers varied from 177 in 2004–300 in 2004, n = 15 years, Fig. 1b) were largely driven by fluctuating group size (according to MNA estimation, annual mean group size = 9·61 ± 0·62 individuals, range = 7·00–11·82, n = 15). MNA estimates indicated that the total population size for the core area remained relatively constant from 1990 to 1997, then rose to a peak in 1999 (Fig. 1b), decreasing thereafter. During the study a total of 8981 individual capture events took place, involving 1859 different individuals. Individual badgers were captured on average twice each year (range of annual means 2·12 ± 0·05). In addition, data were included for 467 animals found dead in the study area.


Figure 1. Trends in population size and M. bovis infection in the Woodchester Park badger population (1990–2004) at social group (a,c) and overall population level (b). The term positive group refers to any group where TB excretion was detected in at least in one individual.

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population and tb excretion dynamics

Although the number of badger social groups in the study area remained relatively constant, the number of incident and prevalent (TB was detected in at least one individual) groups per year generally increased over the course of the study (Fig. 1a). Both mean group incidence and prevalence steadily increased over time (Fig. 1c). Annual TB incidence and prevalence at the population level were negatively correlated with population size (RS = −0·69, P ≤ 0·01: rs = −0·55, P < 0·05, respectively, n = 15 years) and mean group size (RS =−0·81, P < 0·001; rs = −0·71, P < 0·01, respectively, n = 15 years) in the same year. When compared with population size and group size in the preceding year, relationships with TB prevalence remained negative but were weaker (RS = −0·50, P = 0·06; rs = −0·64, P = 0·01, respectively, n = 15 years) and no significant associations were found with incidence.

individual-level analyses

The optimal model is shown in Table 1. The model was fitted with the social group (n = 27) and the year (1990–2004, n = 15) both considered as random factors (Z = 1·51, P = 0·06; Z = 2·27, P = 0·01, respectively). Extra-dispersion scale of the model was 0·685. The prevailing prevalence of TB in a group was significantly associated with the risk of an individual becoming an incident case (Fig. 2a). The risk that a susceptible individual would become an incident case was also higher in groups that were declining in size (Fig. 2b). There was also a statistically significant interactive effect of group TB prevalence and group size trend on incidence, such that the influence of the former was less marked in groups that had undergone extreme changes in size (either positive or negative) than in more stable groups (Fig. 2c). Movement behaviour of the individual was also significantly related to the probability of incidence, such that movement within the core area incurred a greater risk than either not moving or moving into the core area from outside (Fig. 3a). In addition, the group movement index was positively related to the individual risk of becoming an incident case in the following year (Fig. 3b) suggesting that individuals in groups that experienced higher levels of movement were more at risk of becoming incident cases. There were also sex-related differences in the probability of becoming an incident case, such that in female-biased groups, males were at a disproportionately higher risk than females (Fig. 4a). A significant interaction between group sex ratio and the movement index indicated that in groups with equivalent or higher numbers of males the positive association between the group movement index and the individual probability of incidence increased (Fig. 4b).

Table 1.  Results of a GLMM to partition variation in the individual risk of a badger becoming a TB incident case in year t. Parameter estimates for the level of fixed factors were calculated considering a reference value of zero for female level in the variable ‘sex’ and for movement from outside in the variable ‘individual movement’. P-values appear in bold where less than a priori significance limit of 0·05.
Variabled.f.Model estimateF-valueP
  • *

    Individual movement class = no movement.

  • Individual movement class = movement from the core area.

Sex1, 2082−0·85 2·200·14
Age1, 2067−0·17 0·130·71
Group size (year t)1, 514−0·04 2·110·15
Trend in group size (year t − 1 [RIGHTWARDS ARROW] year t)1, 1870−0·01 9·30< 0·01
Sex ratio (year t)1, 1865−0·001 2·250·13
Group TB prevalence (year t)1, 16990·0324·53< 0·0001
Individual movement (year t − 1 [RIGHTWARDS ARROW] year t)2, 20580·67,* 1·54 6·47< 0·01
Group movement index (year t − 1)1, 15472·17 9·61< 0·01
Number of captures (log10) (year t)1, 19851·3510·55< 0·01
Sex × sex ratio1, 20830·21 5·120·02
Sex ratio × group movement1, 2077−0·0412·00< 0·001
Trend in group size × group TB prevalence1, 16220·0002 4·080·04

Figure 2. Results of a GLMM relating individual M. bovis excretion incidence in badgers to (a) the mean prevalence of excretors in the social group, (b) the mean trend in social group size and (c) the mean group prevalence of M. bovis excretors in relation to the trend in group size (the three classes are based on 33% and 66% percentiles of group size trend). Mean values are shown with 95% confidence interval for SE.

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Figure 3. Results of the individual-level GLMM. Incidence (±95% confidence interval for SE) according to individual movement category (a) and group movement index according to individual incident status (b). It should be noted that the partial effects of the other explanatory variables upon mean movement indices are not accounted for.

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Figure 4. Results of an individual-level GLMM, showing interactions involving the social group sex ratio. (a) Displays incidence (±95% confidence interval for SE) relative to sex and social group sex ratio categories (percentiles 33% and 66%). (b) Shows the social group movement index (±95% confidence interval for SE) relative to individual incident cases and the group sex ratio.

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Badgers that became excretor incident cases were more likely to have been ELISA positive in the previous year than those that did not, and these differences remained regardless of whether they had moved within the core area (14·83 ± 5·51% and 45·00 ± 22·37% for negative and incident individuals, respectively, χ2 = 6·3, d.f. = 1, P = 0·01) or not (16·91 ± 1 ·7% and 48·00 ± 10·38% for negative and incident individuals, respectively, χ2 = 255·1, d.f. = 1, P < 0·001). The GLMM to determine whether serological status (ELISA test result) and physical condition in the previous year were related to the likelihood of a badger moving between social groups identified no significant effect after controlling for sex and group size. Individuals moving within the core area did, however, come from groups with higher previous TB prevalence (8·01 ± 1·78%) than did ‘non-movers’ (6·19 ± 0·45%, d.f. = 1, Fanova = 4·77, P = 0·03).

social group-level analyses

Results of GLMMs to partition the variance associated with the risk of a badger social group becoming an incident case are shown in Table 2: model (a) compares incident vs. negative groups (positive non-incident groups were excluded from this analysis, n = 242 group by year combinations), and model (b) compares incident groups vs. non-incident groups (negative or positive but not incident, n = 355 group by year combinations). The models were fitted with social group (n = 27, Z = 1·59, P = 0·06; Z = 0·98, P = 0·16, for model a and b, respectively) and year (1990–2004, n = 15, Z = 1·33, P = 0·09; Z = 1·20, P = 0·12, for model a and b, respectively) as random factors. The extra-dispersion scales were 0·66 for the model comparing incident vs. negative groups and 0·73 for the model comparing incident groups vs. non-incident groups. When comparing incident vs. negative groups (see Table 2a, n = 242 groups by year), the group size trend was negatively related to the probability of group-level incidence (logit parameter estimate =−0·03 ± 0·006). Overall group size, however, was not related to the probability of being an incident group. The proportion of females in the group was found to be significantly negatively correlated with the probability of group-level incidence (logit parameter estimate =−0·02 ± 0·007). Movement index was not found to be significantly associated with incidence, whereas the number of animals moving from another core group was found to be positively associated (logit parameter estimate = 0·58 ± 0·18, mean annual number of animals moving from the core into negative and incident groups were 0·38 ± 0·11 and 0·75 ± 0·32, respectively). No significant association was found between the rate of annual movement and group-level incidence. The model comparing incident groups with non-incident groups (the latter category including negative or prevalent, but not incident cases, see Table 2b, n = 355) yielded similar results to the first analysis.

Table 2.  Results of GLMMs to partition the variance associated with the risk of a badger social group becoming an incident case. Model (a) compares incident vs. negative groups (positive non-incident groups were excluded from this analysis, n = 242 group by year combinations), and model (b) compares incident groups vs. non-incident groups (negative or positive but not incident, n = 355 group by year combinations).
Variabled.f.Model estimateF-valueP
Model (a)
 Group size1, 1180·08 2·880·10
 Trend in group size (year t − 1 [RIGHTWARDS ARROW] year t)1, 232−0·0318·09< 0·0001
 Sex ratio1, 233−0·02 8·21< 0·01
 Group movement index (year t − 1)1, 216−0·61 1·030·31
 Core movements (year t − 1 [RIGHTWARDS ARROW] year t)1, 2230·5810·11< 0·01
 Movements from outside (year t − 1 [RIGHTWARDS ARROW] year t)1, 2280·26 1·0060·32
Model (b)
 Group size1, 119−0·001 0·000·98
 Trend group (year t − 1 [RIGHTWARDS ARROW] year t)1, 330−0·0218·33< 0·0001
 Sex ratio1, 332−0·02 8·35< 0·01
 Group movement index (year t − 1)1, 225−0·33 0·410·52
 Core movements (year t − 1 [RIGHTWARDS ARROW] year t)1, 3240·4710·81< 0·01
 Movements from outside (year t − 1 [RIGHTWARDS ARROW] year t)1, 3340·35 2·250·13

The analysis considering the number of incident cases is shown in Table 3. The model was fitted with the social group (n = 27) and the year (1990–2004, n = 15), both considered as random factors (Z = 2·33, P = 0·01; Z = 1·36, P = 0·09, respectively). The extra-dispersion scale was 0·81. Group-level TB prevalence was found to be positively associated (logit parameter estimate = 0·04 ± 0·009). As in the individual and other group-level analyses, the prevailing trend in group size was negatively related to the proportion of incident cases in a given group and year (logit parameter estimate = −0·01 ± 0·003) while the movement index of the group for the previous year (logit parameter estimate = 1·12 ± 0·51) was significantly and positively related. A significant interaction between group sex ratio and movement index indicated that the relationship between movement and the proportion of incident cases in a group was stronger when the proportion of resident males was higher (parameter estimate = −0·02 ± 0·009).

Table 3.  Results of a GLMM to partition the variation in TB incidence at the level of the badger social group (n = 355).
Variabled.f.Model estimateF-valueP
Group size1, 284−0·02 1·150·28
Trend in group size (year t − 1 [RIGHTWARDS ARROW] year t)1, 342−0·01 5·70< 0·05
Group TB prevalence (year t)1, 3400·0411·22< 0·001
Group movement index (year t − 1)1, 3331·11 4·710·03
Core movements (year t − 1 [RIGHTWARDS ARROW] year t)1, 3370·13 3·000·08
Movements from outside (year t − 1 [RIGHTWARDS ARROW] year t)1, 341−0·12 0·440·51
Sex ratio × group movement1, 336−0·02 7·84< 0·01


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

The influence of the social structure of wildlife populations on the movements of individuals may be complex, and is likely to have significant effects on disease transmission dynamics and persistence (Gudelj & White 2004; Cross et al. 2005). Observational studies of wildlife populations seldom consider such interactions (but see Cross et al. 2004; Brown & Brown 2004) although they are likely to be of profound importance in the management of wildlife diseases. Field studies of badger populations show that culling induced perturbations to the social structure may have substantial demographic consequences (Cheeseman et al. 1993; Tuyttens et al. 2000a,b; Woodroffe et al. 2005) and the results of a recent large-scale field experiment suggest that this phenomenon could be counter-productive to the control of TB in cattle (Donnelly et al. 2003; Donnelly et al. 2006). However, little is known of the epidemiological consequences of the social disruption of badger populations that may drive such counter-productive effects. The current study of a long-term data set from a free-living, high-density badger population focused on identifying the main risk factors associated with the incidence of disease (excretion of M. bovis) at both individual and social group levels. The results strongly suggest that intergroup movements, group structure and stability play an important role in disease dynamics and are consistent with the hypothesis that social disruption may increase incidence.

The single most important factor to explain variations in the risk of an individual badger becoming an incident TB excretor, and in the percentage of incident cases in a social group, was the existing prevalence of excretors in the group (Table 1, Fig. 1a). This is unsurprising given the high levels of sociality among group members, including the shared occupation of setts. The presence of infectious adult female badgers in social groups in the study area has previously been associated with new infections (Cheeseman et al. 1988b), and particularly with the proportion of infected cubs (Delahay et al. 2000b). In addition to direct transmission among group members, it has been suggested that the setts themselves might also act as a potential source of infection (Delahay et al. 2000b; Courtenay et al. 2006).

Despite the importance of existing levels of infection in social groups, the risk of becoming an individual incident case was also influenced by movement, particularly within the core of the study area. Such movement was also associated with the emergence of incident groups and with the proportion of incident cases arising in a group (Fig. 3a,b). These results are consistent with the previous observation that for the period 1978–95 at WP, the incidence of infected badgers increased following years of high rates of between-group movement (Rogers et al. 1998). It seems likely therefore that movement between groups either enhances transmission by increasing the frequency of contacts between previously segregated individuals or is related to the progression of disease to the point of excretion of bacilli. However, the highly aggregated distribution of M. bovis infection in badgers (Cheeseman et al. 1988b; Delahay et al. 2000b) means that not all movements incur the same level of infection risk. In the present study the risk of a group becoming an incident case was related to movements among core social groups but was not associated with movements from outside the core area. This probably reflects the location of a persistent localized cluster of infection in this undisturbed, highly structured core of the study population (see Delahay et al. 2000b).

Moving between social groups may also be a risky strategy for an individual badger because of the increased likelihood of aggressive encounters from other territory holders (Kruuk 1978). This provides an additional potential route for disease transmission through bite wounding, which has been suggested to lead to a more rapid progression of disease and excretion of bacilli (Gallagher & Nelson 1979; Clifton-Hadley et al. 1993). In addition, the physiological stress of moving between social groups could contribute towards the development of disease in individuals, by, for example, reducing immunocompetence and permitting the reactivation of latent infections (Gallagher & Clifton-Hadley 2000). Particularly, during periods of reproductive activity, individuals may experience heightened physiological stress and their immune systems may be weakened (Griffin & Thomson 1998). Such seasonal effects could influence movement (see Discussion below) and also disease susceptibility, potentially triggering active excretion of previously latent infection (see Gallagher et al. 1998).

The importance of between group movements of individuals raises the question of why animals move. Although a detailed investigation of the factors that may predispose individuals to move is beyond the scope of the present study, we carried out preliminary analyses to determine whether prior exposure to M. bovis (inferred from a positive ELISA result) or body condition were likely to be influential. Although individuals with a positive ELISA result in the previous year were more likely to become incident excretor cases, neither previous exposure or body condition were associated with the likelihood that a badger subsequently moved groups. Nevertheless, susceptible individuals that moved within the core area were likely to have been resident in a group with a higher prevalence of TB excretors than the resident groups of badgers that did not move. This raises the possibility that the presence of TB in a group could promote dispersal to avoid infection, a phenomenon that has been observed in badgers (Butler & Roper 1996; Roper et al. 2001) and birds (Brown & Brown 1992) in response to ectoparasite burdens. However, to our knowledge, there is no evidence in the literature of a similar phenomenon in microparasite infections.

The present study describes a chronic disease persisting within a population that exhibits social structure, whereby infectious contacts may be frequent within groups, and may spread spatially by host movement between groups (see also Cross et al. 2005). The high degree of social structure in badger populations could promote persistence of TB by allowing infection to occur asynchronously in various groups and avoiding deep ‘global’ troughs (Bolker & Grenfell 1996). This is supported by previous work that indicated that temporal trends in TB in the WP population were not synchronized among neighbouring groups (Delahay et al. 2000a). The balance between TB extinction across groups and group recolonization (referred to in the paper as group incidence) allows for disease persistence in WP (Fig. 1a). In the present study the number of incident groups (patches) did not exhibit dramatic oscillations, varying from 1 to 5 per year (Fig. 1a), and later appearing to stabilize at about four groups per year. The number of prevalent groups tended to stabilize at about 12–14 of the 23–27 social groups present each year. Hence, infection remained present in the population in a relatively high number of groups throughout the study period, but apparently disappeared from some, before re-emerging sometimes several years later. While relatively low levels of intergroup movement (Rogers et al. 1998) appear to limit the rate of between-group transmission in this highly structured population, the chronic characteristics of the pathogen ensure that infection persists.

The risks of becoming an incident case at both the individual and group level were also associated with social group dynamics and structure (Fig. 2b). For example, groups that had diminished in size since the previous year were more likely to become incident groups. In groups that underwent substantial changes in size (either increases or decreases), the influence of the pre-existing prevalence of TB on individual risks of becoming an incident case decreased (Fig. 2c). The study population was stable in terms of social organization as indicated by the relatively consistent number of social groups. In spite of this however, pronounced trends in population dynamics and disease incidence and prevalence were observed. These findings differ in some respects from those of previous studies (e.g. Rogers et al. 1997; Delahay et al. 2000b), which reflects the inclusion in the present study of more recent data, and highlights the inherent value of long-term data sets in identifying demographic and epidemiological patterns that may operate over many years. It is particularly notable that after 1999 there was a generally negative trend in the WP badger population size, driven by changes in group size. Simultaneously the percentage of incident cases observed gradually increased (Fig. 1b), with the result that these phenomena became significantly negatively correlated. Reductions in the size of some social groups in WP observed during this study occurred in the absence of any known human disturbance. Groups that were either decreasing or increasing in size received a greater number of movements from the core area than groups that remained stable. In such circumstances the potential influence of intergroup disease transfer on the probability of subsequent incident cases might be greater than that of the pre-existing prevalence of infection in the group. This could explain the significant interaction observed in the present study.

During the present study, social group size varied substantially over time and between groups but was not significantly correlated with either the risk of becoming an incident group or the percentage of resident incident cases. Modelling studies have suggested that infection will only persist in groups above a certain threshold size (Smith et al. 1995; White & Harris 1995). However, the influence of social structure and movement may confound this simple relationship (Cross et al. 2005; Lloyd-Smith et al. 2005) and impede disease invasion and persistence (Loehle 1995). Results from the present study suggest that transmission and persistence of TB in this badger population may be more constrained by levels of transfer of individuals between groups than by group size. Hence, it may be the process of group size change rather than group size itself that has most influence on levels of infection. In this context it is interesting to observe that Roper, Ostler & Conradt (2003) found that the process of dispersal in badgers often involved an intermediate stage during which an animal might move back and forth between two groups before making a permanent change. Clearly this process would enhance the opportunities for transfer of infection as a consequence of dispersal.

The sex ratio of social groups was also related to the risk of an individual becoming an incident excretor, and its influence varied between the sexes (Fig. 4a). When there were more females present in a group, males were disproportionately more likely to become incident cases than females (Fig. 4b). Consequently, as the adult sex ratio of a group became more female biased, the risk of it becoming an incident case increased. This may relate to the observation that males are more likely to move to social groups with a higher proportion of females in residence (Rogers et al. 1998), and that infection may be more prevalent in males (Gallagher & Nelson 1979; Cheeseman et al. 1988b). Previous work has suggested sexual dimorphism in susceptibility to infection and disease (e.g. Zuk 1990), with males tending to be more susceptible. Alternatively (or additionally), sex differences may relate to the different behaviour of males and females in activities related to breeding. Susceptible male badgers were significantly more likely to move (this was the case for 58·8% of cases moving within the core area and only for 40% of susceptible cases that stayed in the social group). As males generally exhibit higher levels of bite wounding than females (Gallagher & Nelson 1979; Delahay et al. 2006) they may be more susceptible to infection via this route, particularly while making extra-territorial movements. In addition, a higher proportion of females in a group could increase the number of potential breeding opportunities for males, and promote aggressive competitive interactions between males, and also the risk of horizontal female–male transmission. Male badgers are probably more actively involved in territorial defence, which may involve aggressive encounters and enhance opportunities for between-group transmission (Cheeseman et al. 1988a; Rogers et al. 1998). The observation that a male-biased sex ratio was related to a more marked positive relationship between the group movement index and the individual risk of being an incident case in the following year (Fig. 3b) may reflect their potential importance for between-group transmission. On the other hand, females may be more important in the maintenance of infection within groups. Hence the pattern of incidence found in relation to individual sex and group sex composition is broadly consistent with sex-related differences in movement, territorial and reproductive behaviour (e.g. Roper, Shepherdson & Davies 1986).

The long-term study of the WP badger population has produced a unique and valuable data set for the investigation of disease dynamics in wildlife. However, field methods for the study of wild mammals and the diagnosis of M. bovis infection in live animals are imprecise and impose limitations on interpretation of the data. For example, live-trapping records provided information on the movement of badgers between social groups, although this can only provide a relative measure of when and how often badgers moved (Rogers et al. 1998). Similarly, contemporary tests for the diagnosis of M. bovis infection in badgers are relatively insensitive (see Delahay et al. 2000b), and so are likely to underestimate true levels of infection. Nevertheless, these limitations should not affect the main conclusions of the present study, as the repeated sampling of a large number of individuals and the duration of the study should allow broad trends to be identified.

Overall, the findings indicated that within-group dynamics, movement rates and the structure of host contacts were more important in driving disease incidence than density-dependent transmission in isolation (Ball & Neal 2002; Cross et al. 2005). The evidence suggests that movement of individuals between groups may be instrumental in driving disease dynamics at the population level, and adds further support to the contention that the social disruption of badger populations, for example by culling, is likely to promote disease spread. Past badger culling policies have been accompanied by an inexorable rise in the incidence of TB in cattle. Indeed, it has become apparent that the various strategies may actually have been a contributory factor to the increase in disease through the phenomenon of perturbation (Woodroffe et al. 2005). The results presented in this paper lend weight to this argument, demonstrating that stable social structure at least mitigates against new incident cases of disease. Hence, the development of successful strategies for the control of TB in badgers and transmission to cattle will require serious consideration of the likely impact of any interventions on badger social organization.


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

Thanks to the Woodchester Park team for carrying out fieldwork, the staff of the Bacteriology section of VLA and the Microbiology team at CSL for expert technical assistance. We are also indebted to all the farmers and landowners in the Woodchester Park study area for their co-operation during fieldwork. We also thank John Gallagher, Richard Clifton-Hadley, Fiona Stuart, Sheila Crispin and two anonymous referees for providing useful comments. J. Vicente was supported by a Postdoctoral research grant from JCCM. The project was funded by the Animal Health and Veterinary Group of DEFRA.


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