The maintenance of infectious diseases requires a sufficient number of susceptible hosts. Host culling is a potential control strategy for animal diseases. However, the reduction in biodiversity and increasing public concerns regarding the involved ethical issues have progressively challenged the use of wildlife culling. Here, we assess the potential of wildlife culling as an epidemiologically sound management tool, by examining the host ecology, pathogen characteristics, eco-sociological contexts, and field work constraints. We also discuss alternative solutions and make recommendations for the appropriate implementation of culling for disease control.
Infectious diseases that can be transmitted both within and between wild and domestic animals and human host populations represent almost 75% of the infectious diseases that have (re-)emerged in human populations in the last century1. As these pathogens are typically transmitted to multiple host species2, wildlife are often an important component of such systems3, as it is illustrated by the novel coronavirus SRAS-COV-2 which is thought to have emerged in seafood markets in Wuhan, China4.
Infections from wild animals can be directly transferred to humans, as illustrated by Human immunodeficiency virus-1 (HIV-1), which causes acquired immune deficiency syndrome (AIDS) in humans as the result of an independent cross-species transmission event of simian immunodeficiency viruses (SIVs), which infect African apes5. Vectors can be the link between different populations (i.e., wild animals and humans). For instance, the yellow fever virus is transmitted from monkeys to humans by mosquitoes6. Wild animals can also transmit pathogens that are harmless to humans but harmful to domestic animals, with often significant impacts on animal health and agricultural economics, as exemplified by foot-and-mouth disease (FMD). Finally, wild animals and humans can also be linked through extended transmission chains via domestic animals, without vectors, as illustrated by influenza transmission through humans, pigs, and wild and domestic birds7. Although it was found that emerging infectious diseases were 4-fold more frequent in the 2000s than in the 1940s8, pathogen spillover among different species still remains a rare phenomenon that requires the fulfillment of different conditions9. Particularly, the interspecific interactions between donor and recipient species must be frequent enough and the pathogen has to be sufficiently pre-adapted to the recipient host species. Consequently, proximity is a key driver of pathogen spillover among species, especially for directly transmitted viral diseases10. Anthropogenic pressure on the natural environment has led not only to the fragmentation of natural habitats and the extinction of numerous species11,12, but also to an encroachment of farming and urban activities at the periphery of areas occupied by wild animal populations13,14.
As soon as the concept of pathogen transmission started to be understood, culling of domestic animals was put in place to manage animal diseases, due to its apparent simplicity. Indeed, it was used for the management of the first rinderpest epidemics in Europe in 1709 in addition to movement restriction, cordons sanitaires, quarantine, disinfection, and carcass burying15. Later, vaccination campaigns resulted in the successful eradication of rinderpest16. The objective of animal culling is to reduce the rate of infectious contacts below the threshold required for pathogen persistence by decreasing the number and density of infectious and susceptible animals17. More recently, culling of cattle, sheep, and pigs was implemented to control the FMD epidemic in the United Kingdom (UK) in 200118, of poultry to limit the transmission of highly pathogenic avian influenza viruses in 200519, and of pigs to control the African swine fever epidemic in China in 201820. During the 2001 FMD epidemic, 4 million domestic animals were culled for the purpose of disease control, and at least a further 2.5 million animals were destroyed in ‘welfare’ culls21. The very high economic cost and wider impacts on society of this culling strategy raise the question of the best approach to adopt in future epidemics.
Wildlife culling also has been used for disease control. During the 20th century in southern Africa, culling was implemented to create wildlife-free corridors around national parks to protect cattle farms from the spread of trypanosomiasis22. About 660,000 animals from 36 species, including large mammals such as black rhinoceros (Diceros bicornis) and elephants (Loxodonta africana), were killed23. Rabies control in Europe included red fox (Vulpes vulpes) depopulation schemes24 that were undertaken from the 1960s, with limited success compared with oral immunization25. More recently, culling has been used to reduce populations of European badger (Meles meles) in the UK and the Republic of Ireland, of brushed-tailed possum (Trichosurus vulpecula) in New Zealand, and of African buffalo (Syncerus caffer caffer) in South Africa because these species were infected with Mycobacterium bovis, the zoonotic pathogen responsible for bovine tuberculosis (Table 1).
Currently, the conclusions of the final report by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services suggest that around 1 million species already face extinction, and many more within few decades26. This implies an increasingly parsimonious management of wildlife. In many settings, culling is no longer considered an acceptable policy option for disease control because it significantly affects biodiversity conservation and more generally ecosystem functioning27. Moreover, removing wild animals from natural populations can have unexpected counterproductive consequences on pathogen transmission within the host community. Finally, depending on the species targeted for culling (e.g., protected, pet or livestock species), the public response to culling-based control options can facilitate or hinder their implementation. Consequently, the cost-effectiveness and cost-benefit balances of some wildlife culling options is now a topic of intense debate among scientists, policy makers, stakeholders, and the general public (Table 1). In this review, we assess the evidence regarding wildlife culling as a potential control strategy in several epidemiological contexts, compared with other available control options (see Supplementary Fig. 1, Table 1 and the Supplementary Information for article selections from 1992 to 2018). We describe socio-ecosystem and infectious disease dynamic features that must be understood in order to design effective culling policies. Particularly, we review the range of potential consequences of culling, including its counterproductive effects on the disease system. Finally, we discuss wildlife culling relative to alternative disease control policy options.
Ecological, epidemiological and eco-sociological aspects of wildlife culling strategies
The design of a culling strategy requires the identification of the species and individuals to be culled as well as the spatial and temporal extent of the culling. Culling can have various forms, from the most extensive (i.e., culling the whole target population)28, to the most selective (i.e., removing only the infected individuals; i.e. test and cull)29 (Fig. 1a and Box 1). Such choices should be informed by an evidence-based understanding of the focal eco-epidemiological system (Fig. 1).
Determining the target species
The identification of the species involved in disease transmission is an important step for designing control strategies. Pathogens can have narrow animal host ranges, such as the Middle East respiratory syndrome (MERS) coronavirus that emerged in humans in the Middle-East in 2012 and that circulates, based on the current knowledge, only in dromedary camels30. Others can infect a broad spectrum of animal hosts, such as M. bovis (see Table 1 and ref. 31), which can infect a wide range of even-toed ungulates. When the host range is broad, culling only one host species can produce unexpected effects. In this situation, it is important to identify the competence (the ability to maintain and transmit infections) of each host community, and to adjust the culling strategy accordingly.
Johnson et al. gave an excellent empirical demonstration of the role of the host community, species richness, and host competence in disease transmission32. They showed that host diversity inhibits the virulent pathogen Ribeiroia ondatrae and reduces amphibian disease (78.4% decline in transmission in richer assemblages). In a different system, Dearing et al. showed that the Sin Nombre virus prevalence in deer mice is related to the mammalian community complexity. The prevalence was lower in more diverse communities, where deer mice had fewer intraspecific interactions, than in less diverse communities33. Failure to capture the complexity of the host epidemiological community can jeopardize disease control. Large-scale proactive and localized reactive culling strategies were implemented only in badger populations to control the M. bovis spillover to cattle in the UK, although other wild species present in these ecosystems, such as deer, are sensitive to the disease and can excrete the bacteria34. This control strategy gave mixed results (see Table 1 and Boxes 1 and 2 to understand these findings, and refs. 35,36,37,38). Therefore, after the detection of the disease in France in wild ungulates in 2001 and in cattle in 2002, a multi-host culling approach that included deer, wild boars, and badgers (Table 1) was adopted to limit the spread of bovine tuberculosis in wildlife39. This example highlights the importance of determining the role of each influential host species for understanding the transmission system epidemiology40,41. Within a community, different species interact in a geographic area and have interdependent densities, according to ecological processes (e.g., predation, competition, and parasitism)42,43. If a species is weakly connected inside the community (i.e., contact rates and disease competence are low), the impact of its removal on the ecological functions of other species will be limited. However, the ecological niche released by this removal may make available resources for competitive species44 that may be more connected and/or competent. Conversely, reducing the density of a highly connected species could disturb the whole ecosystem (e.g., keystone species45) with unpredictable epidemiological consequences.
Assessing the population size
If the target host population is large, it may not be possible to cull enough individuals to limit pathogen transmission. This could apply to large bat colonies of central Africa, where they are suspected of being reservoirs for the Ebola and Marburg viruses46,47, and of Latin America, where bats can transmit rabies28. When the southwestern United States and Mexico are considered together, the census population size of the roosting colonies of Mexican free-tailed bats (Tadarida brasiliensis mexicana) may easily reach 107–108 individuals48. Conversely, if the host population is very small, culling might generate conservation concerns (Box 3). Population dynamic models can be used to predict whether culling could lead to a population crash.
Before culling actions are undertaken, size estimation of the target population must be combined with an evaluation of the culling rate required to decrease the prevalence or to eliminate the pathogen in order to determine the number of individuals that should be culled. At this stage, it is important to account for uncertainty in the size estimation of the target population. For example, to control bovine tuberculosis in 2015, licensees in Dorset county (UK) were required to cull at least 615 badgers, which corresponded to the recommended 70% culling rate49 for the lower bound of the 95% confidence interval for the size estimation of that population (879–1547 animals). However, if the actual population size were close to the upper bound of the estimation, culling 615 badgers would have resulted in a reduction by 39.8% of the population size, which would not have been sufficient to control the spread of bovine tuberculosis in cattle. Numerous methods, models, and tools have been developed by ecologists to estimate population abundance (see Box 1 for more details).
Identifying the individuals to be culled
In the simplest theoretical case, each infected individual in a population is equally likely to transmit the infection. In reality, the risk of onward transmission between individuals even within a particular species can greatly vary (e.g., super-spreaders in host populations)50,51. Such heterogeneity can result from individual characteristics, including immune status and behavior52. Selective culling strategies can target individuals on the basis of their infection status (e.g., targeting only infectious individuals) or their demographic characteristics, such as age or sex (see Box 1 and Fig. 1). An important and sometimes overlooked aspect of culling for disease control is the identification and prioritization of populations or individuals that have the greatest potential to transmit the infection53. In reality, the culling rate is often higher for individuals that are easier to capture and/or kill. When such individuals are also more likely to be immune and resistant to the disease, this can interfere with the establishment of herd immunity, which is a natural barrier to transmission29,54,55. This is the case of culling campaigns to control the classic swine fever in wild boar where old individuals that are often immune and resistant are more easily eliminated56.
Understanding spatial distribution and connectivity
The habitat of a wild animal population is often fragmented, either naturally, or as the result of human-driven modifications of the landscape57. The populations occupying such habitats are structured in sub-populations that may be connected through dispersal. The dynamics of the resulting meta-populations are governed by local dynamics and also by the colonization and extinction processes that determine patch occupancy. In fragmented wildlife populations, the culling objective could be to reduce the size of sub-populations below the critical community size, which is the minimum population size required to maintain the pathogen58,59 (see Box 1 and Fig. 1b). However, a pathogen can become extinct within some sub-populations60, but may persist in other cryptic sub-populations that are impossible to cull and can play the role of reservoir29. In the case of Tasmanian devil culling, such cryptic sub-populations may have acted as reservoir for immigration of infected individuals from areas without culling in Freycinet peninsula on Tasmania east coast, between 2004 and 200829.
Understanding transmission dynamics
For several infectious diseases in wild animal populations transmission increases with host density61. For other infectious diseases, the transmission risk depends on the frequency rather than on the density of infectious hosts62 (e.g., sexually transmitted diseases in humans63). Intra- and inter-specific transmission also could be differentially influenced by density and frequency, and, therefore, this aspect needs to be thoroughly characterized. Understanding whether transmission is frequency- or density-dependent (see definitions in Box 1) is important for the development of an efficient culling strategy. Indeed, only culling strategies that target infectious individuals or individuals that are most likely to become infectious would be efficient for the control of frequency-dependent diseases. Conversely, untargeted culling strategies might be efficient for density-dependent diseases. However, whether disease transmission is density or frequency dependent can be difficult to determine29,64. The analysis of epidemiological and genetic data combined with mechanistic models allows testing different transmission scenarios (e.g., density-dependent, frequency-dependent and/or environmental transmission) and can provide valuable insights into pathogens dynamics65.
Furthermore, the structure of the underlying contact network is affected by the host ecology, the pathogen species, and the spatial structure. Consequently, there is often considerable uncertainty on the transmission risks within a given eco-epidemiological system. For instance, it was hypothesized that rabies transmission in vampire bats in southern America was density-dependent due to the large size of the colonies. However, an empirical study showed no relationship between host density and seroprevalence28. Even in large colonies, any single animal has a limited number of neighbors to bite, and infectious contacts may not be homogenous due to the social structure within the colony. In Peru, the rabies virus might be maintained in bats through frequency-dependent transmission with a key role for juvenile and sub-adult bats, leading to the observed inefficacy of culling to eliminate transmission within bats and also in humans and domestic animals28. In discontinuously distributed host populations, frequency-dependent transmission and endemic local dynamics in some sub-populations can coexist with density-dependent transmission and epidemic local dynamics in other sub-populations, as observed for rabies in foxes66. A recent study on rabies in Tanzania highlighted the importance of the environment (e.g., roads, rivers, elevation, and dog density) besides epidemiological and genetic data. This combined approach showed that the localized presence of dogs was the most important determinant of rabies diffusion, and not their density. This implies that culling is ineffective for rabies control67.
Finally, indirect pathogen transmission (i.e., environmental transmission65) is often overlooked in disease management, particularly when planning culling strategies, despite the fact that it might limit the effect of culling on transmission. When pathogens can survive in the environment, they might persist for longer and their control is more difficult to achieve. Environmental transmission is most likely involved in the failure to control chronic wasting disease (CWD) in deer populations in America68 (see Supplementary Information—case studies), and might also explain why, in America, brucellosis persists despite the very small population size of bison (Bison bison) (e.g., less than 200 individuals), one of its reservoirs69 (see Supplementary Information—case studies).
Space and time
Determining spatial-temporal scales
Most often, the population targeted for culling occupies a large territory and the geographic scope of the culling action must be specified. The locations of culling and non-culling areas (see Fig. 1a and Box 1 for definition) have significant consequences on the disease spatial distribution and transmission dynamics70. Hot spots of disease transmission are typically prioritized, although the establishment of a transmission firebreak might require culling also at some distance from the high incidence areas. Conceptually, in order to robustly estimate the culling impact, similarly structured areas (for instance, in terms of disease risk, animal populations, vegetation, altitude, temperature, precipitation) need to be identified and monitored. The immediate surroundings of a culling area, often called the buffer area, also should be taken into account for measuring the impacts of culling71 (Fig. 1a). For instance, the Randomized Badger Culling Trial in UK (Box 2) showed that repeated, widespread culling could reduce bovine tuberculosis incidence in cattle over 100-km2 areas, but that its incidence increased in areas immediately surrounding the culling areas37.
Regarding the temporal scale, empirical evidence suggests that culling may be effective as a short-term response to highly localized disease outbreaks72. However, a temporary use of culling has rarely produced significant long-term effects73. For instance, in culled areas of the UK, the badger population was back to pre-cull densities after about 10 years74.
Defining culling rate and periodicity
In addition to spatial-temporal scales, the target culling rate and periodicity have to be considered when developing a culling strategy64. For this, approaches relying on epidemiological modeling are often used. Depending on the initial prevalence and incidence rates in a population, different scenarios are modeled by varying parameters, such as the population reduction rate and the cull duration and frequency. The model that maximizes the likelihood of pathogen local extinction is selected75. However, such approaches are typically hampered by the limited knowledge of the eco-epidemiological system and transmission processes76. For instance, models often consider that host and pathogen populations are well mixed, with homogeneous disease transmission among susceptible and infected individuals. However, it is now well known that transmission may vary over time, in relation to social structures, or depending on individual characteristics, such as age. Also, modeling can lead to the conclusion that an efficient culling strategy cannot be implemented. For instance, modeling studies on Tasmanian devils and devil facial tumor disease (DFTD) (Box 3) showed that culling every 3 months was insufficient to suppress the disease in isolated populations. More frequent culls are more likely to be effective, but the required target culling rate might be too high to be achievable77, or could threaten the host population survival.
Quantifying the necessary economic resources
Often, control strategies to manage disease reservoir populations are designed from an eco-epidemiological perspective, but ignore the economic trade-offs78, despite their importance, especially in low-income settings79. From a purely economic perspective, culling should be undertaken whenever cost-benefit analyses show that increased revenue from reduced disease risks exceeds the (direct and indirect) costs of culling, and culling performs better in the cost-benefit analyses than other disease control strategies80. However, cost-benefit evaluations often reveal that culling strategies are not economically sustainable. For instance, the quarterly culling of the Tasmanian devil population in the framework of DFTD control cost 200,000 Australian dollars per year per 100 km2 over almost 3 years without significant decrease in pathogen prevalence (see Box 3)77. More frequent removals would be required, but they are hardly achievable economically. For each culling strategy, the cost-benefit balance needs to be assessed over a range of culling rates and periodicity options. Such assessment might reveal that the ‘do nothing’ option is often better in terms of cost-benefit81. Haydon et al. examined the role of mathematical models in the implementation of approaches that went beyond the traditional control strategies of the 1960s (i.e., movement ban, disinfection of infected properties, and rapid slaughter of all animals within a flock/herd) to control the 2001 outbreak of FMD in the UK21. The direct and indirect economic costs of the 2001 epidemic are estimated at £3 billion and £5 billion, respectively. This, together with the widespread public concern at the visible slaughter of millions of animals, prompted a major revision of outbreak contingency planning21. For badger control, the Department for Environment, Food and Rural Affairs in the UK estimated a cost of £16,087,000 from 2013 to 201782 (see Box 2 for epidemiological context and results). England has recorded 3888 new tuberculosis bovine cases in 201783, compared with fewer than 100 per year in the 1980s35, with a total of 31,773 animals slaughtered, and a 7% increase year-on-year83.
Exploring culling social acceptance
Wildlife culling to decrease disease incidence in domestic animals is often not culturally acceptable, and its acceptability may vary considerably among ethnic groups69. Alternative options to wildlife culling (see Box 4) are increasingly discussed in ethical committees in terms of animal welfare84. The citizens’ feelings toward the targeted species could also limit the success of culling-based control strategies. For instance, badgers are a protected species in the UK and are highly regarded by much of the population85. Badger culling has triggered vigorous protests in the UK86. In the Republic of Ireland, the use of restraints (snares) to avoid dispersion of animals from culled areas led to a more cost-effective badger culling, but it is considered unacceptable for animal welfare in the UK87 (see Box 2). Societal perceptions also affected the use of culling strategies to control brucellosis in North America (see Supplementary Information—case studies). Elk, the main reservoir for brucellosis88, are generally appreciated and allowed to roam outside national parks with very few restrictions. Conversely, bison are less valued and they are slaughtered outside conservation areas, although they are a less infectious reservoir55.
Fieldwork constraints and limits
Culling a sufficient proportion of individuals in the population is a key determinant of the culling strategy success. However, wildlife culling is often technically difficult (i.e., field access, animal behavior). For instance, the Alpine ibex (Capra ibex) culling in the massif du Bargy in France involved carcass removal for incineration by helicopter, in order to avoid environmental and scavenger contamination89. Continuous culling is particularly challenging logistically, but also due to the animal adaptation to the culling technique. This can compromise the culling objectives identified through modeling. Culling work can be hampered also by the field teams’ exhaustion, which sometimes results in a progressive decrease in the capture rate77. One study estimated that the culling efficiency rate within the Randomized Badger Culling Trial in the UK (see Box 2) ranged between 32% and 77%74, whereas some dynamic transmission models indicated that 80–100% of animals should be removed87,90. Consequently, some researchers concluded that if it is not possible to guarantee a sufficiently high removal of individuals, a non-culling strategy may be more effective in terms of prevalence reduction and less costly than a culling strategy with limited culling efficiency46. However, ecological expertise can increase the likelihood of catching animals. This was the case for badgers when researchers found that the weather (rain and temperature) influenced the trapping success91.
Surveillance, diagnosis, and rapid detection
One major limitation of culling, especially in a test-and-cull strategy (see Box 1 and Fig. 1), is the capacity to accurately detect the pathogen or the disease in the targeted population or in individuals. Technical capacities are often suboptimal and diagnostic tests are rarely available for accurate pathogen detection in wildlife, especially for emergent pathogens. CWD control in deer and elk populations in America is a good illustration of the difficulties associated with the diagnosis of infectious diseases in wildlife92 (see Supplementary Information—case studies). The appearance of clinical signs in infected animals might take more than two years. Moreover, the available diagnostic tests for urine, saliva or feces are not optimal, with diagnostic sensitivities of 39%, 78%, and 53% respectively, and specificity close to 100% for the three sample types93,94. Therefore, an infected individual can remain in the population for a long time without being detected and removed. No prevalence decrease has been observed after 3 years of intensive to intermediate culling in north-western Colorado (USA) between 2001 and 200564,92,95. However, the development of diagnostic tools for the identification of infected animals has been steadily progressing as well as sampling techniques, with a shift from post-mortem to ante-mortem approaches targeting peripheral tissues and bodily fluids93.
In many cases, diagnostic tests are based on antibody detection, for example for brucellosis, a chronic disease in the Alpine ibex (C. ibex), and therefore will miss some current active infections. It was estimated that only 51% of all seropositive Alpine ibex (C. ibex) individuals excrete the bacteria96. The development of rapid tests might lead to animal euthanasia directly in the field after their capture, or after recapture if laboratory analyses are required97. However, an undesirable consequence of the use of antibody tests is the removal of individuals that were infected but have recovered and are now immune. Slaughtering seropositive animals may reduce the herd immunity by removing naturally immunized individuals from the population55. Moreover, a striking example of the potential consequences of poor test specificity is that 54% of bison individuals culled to control brucellosis in Yellowstone showed no post-mortem sign of brucellosis55. Culling many uninfected individuals could jeopardize the persistence of an already small and endangered population (see Supplementary Information—case studies). Therefore, for successful disease control, a test-and-cull approach could be adopted only when good diagnostic tests become available for that pathogen and its infectious period is precisely characterized.
Predicting the culling response
Territoriality, behavior modifications, social structure perturbations, and emigration
In natural systems, the social and physiological plasticity of animal populations targeted by a culling campaign can give them a remarkable capacity to adapt and recover after culling98. Culling effects go beyond the simple reduction of the population numbers. Animal territoriality is often affected by culling, with crucial consequences on the epidemiology of the targeted pathogen, as recently modeled by Prentice et al.75. The behavioral response of badgers to culling in the UK (see Box 2) included increased dispersal within the culled areas. This resulted in an increased overlap of badger social group territories and was associated with increased M. bovis prevalence among badgers inside targeted and neighboring social groups99,100. Carter et al. showed that the territoriality of badger populations could be perturbed for up to 8 years after the culling action74. The same phenomenon of home range perturbation probably occurred in Alpine ibex (C. ibex) populations after culling in response to brucellosis re-emergence in adjacent livestock and humans96. Therefore, culling modifies the social organization of animal groups, but can also alter the sex ratio, age, and dominance structures and stimulates the survivors’ dispersion in new areas101 (Fig. 1c). Moreover, culling can increase stress, leading to immune system depression and higher disease expression102.
Compensatory reproduction, migration, and community species adaptation
In most mammals, population dynamics are driven by density-dependent fecundity and mortality. Because of resource limitations, the demographic parameters related to their reproduction, survival and dispersal vary in function of their own density and also of other species within their ecological community. This results in a regulatory relationship between population growth rate and density in which populations grow when density is below the ecosystem carrying capacity, and decline in the opposite situation (Fig. 1d)103. Thus, culling can be partially or fully compensated through these mechanisms, and this could in turn lead to an increase in pathogen transmission98. In temperate areas, reproduction seasonality must be taken into account to determine the optimal culling timing. Culling just after the birth pulse and not shortly before the breeding season might limit reproductive compensation phenomena that could increase the number of susceptible individuals and the transmission risks61. Culling can also promote immigration into an area with decreased local density and increased resource availability (Fig. 1d)74,104. For example, a mass immigration of mice originating from several kilometers away was observed after the culling of deer mice to control Sin Nombre Virus. The entire culled population was replaced within 2 weeks105.
Frameworks for decision-making
Before exploring the possible approaches for complementing or replacing culling as a disease control strategy, we will summarize seventeen elements that might increase the likelihood of successful disease control through culling (see Fig. 2). The first six key elements for the efficient implementation of host culling to eradicate a disease were identified by Myers twenty years ago106: (1) having the resources to conduct of the whole project; (2) having access to all the necessary areas (public or private); (3) the target species has to be sensitive to the action; (4) post-culling immigration has to be limited; (5) the pathogen has to be detectable at low prevalence; and (6) ecosystem management may be required after the potential eradication of a ‘keystone’ target species. We propose additional elements that should be considered when designing a culling strategy: (7) the target species has to be the only, or at least the primary component of the wildlife chain of the pathogen transmission/reservoir; (8) the target number of individuals to be culled has to be achievable; (9) the target species has to be easy to catch and to cull; (10) infectious individuals should be preferentially removed and immune ones should ideally be left in the system; (11) pathogen transmission should be mainly density-dependent; (12) the areas selected for culling should be areas with the highest disease risk or highly connected to other sensitive areas; (13) control areas without culling need to be considered to evaluate culling effectiveness; (14) the planned culling duration should be reasonable; (15) the culling rate and periodicity have to be achievable; (16) the civil society should understand and approve the action; and (17) enough human resources must be provided in order to avoid field team exhaustion. However, due to the limited success of the culling strategies implemented to date in different species and different ecosystems (Table 1), other policy options should be considered before culling. These options are summarized and compared with culling in Box 4.
Conclusion and recommendations
Effective culling strategies for wildlife pathogens require a number of conditions that are challenging to meet when all constraints are considered (see Fig. 2). Importantly, culling can lead to counterintuitive and detrimental outcomes, for instance higher disease incidence in some areas36,37. Therefore, a decreased disease risk is far from being a guaranteed result with culling.
Ecological, sociological, and epidemiological contexts have to be fully considered to inform the choice, design, and the final decision on such a strategy. Before choosing culling, it is particularly important to consider all involved host populations and to evaluate their contribution to pathogen maintenance and transmission29,41. This review demonstrates that culling wildlife reservoir populations might ultimately exacerbate the spread of a given disease, depending on the situation54,72,73,81,107. Such negative effects are difficult to anticipate due to the complexity of the relationships between host density, host contact rates, and disease incidence. In addition, wildlife populations are often inaccessible, which makes their demographic and epidemiological characterization difficult. Before wildlife culling is undertaken, pathogen transmission pattern, host contact pattern, regulatory processes, seasonality, spatial structure, and environmental sources of infection should be precisely understood46,80. Today, scientists call for the creation of evidence-based syntheses for policy makers that are inclusive, rigorous, transparent, and accessible: “Rapid synthesis can respond more tactically to emergencies or, more commonly, to the day-to-day business of government. It can involve rapid evidence assessments, which are more targeted than a systematic review, with more-restricted search terms, evidence-gap maps and semi-structured interviews - techniques which ensure that more voices and views are considered and weighed, and which go beyond what a scientist would typically consider a ‘review’”108.
Management strategies should be multifaceted and adaptive through space and time109. The design of an efficient and acceptable culling strategy would require a systems-based approach built on the mechanistic understanding of the system key components and their interactions, giving careful consideration particularly to the range of species involved. Surveying a wide network of transmission hubs will facilitate the identification of major and minor host species, and will give a greater understanding of the transmission risks (Fig. 3). Non-invasive methods for pathogen detection in wildlife are needed. The recent development of techniques for specimen collection and pathogen detection from the environment110, oral fluids (i.e., saliva111), feces112, and blood from blood-sucking flies113 constitutes an important scientific breakthrough. It has the potential to open the black box of wildlife infectious disease dynamics. These new approaches could dramatically decrease the costs related to wildlife fieldwork and long-term longitudinal surveys of populations from different species and/or in risky areas. They could also allow increasing the spatial range of epidemiological surveys, and thus the likelihood of catching pathogens for genetic analyses and for understanding the evolution of pathogen transmission among hosts88.
When efficient (inducing long-term immunity) and easily administrable vaccines are available, vaccination may be a more appropriate option, although the cost will be an important factor to consider. Immunization results in the reduction in the frequency of susceptible individuals in the population, whatever the pathogen transmission mode. However, tools for the differential diagnosis between vaccine- and infection-induced antibodies are needed to monitor the natural infection dynamics in the population.
Finally, wildlife culling should only be considered as an option for disease control by reducing the pathogen’s ecological niche114, in combination with the complementary and alternative approaches detailed in Box 4115.
The study of ecosystems is likely to provide insights into the consequences of culling and other disease control policies on host behavior and up to landscape-level patterns of transmission risk. It should stimulate the cooperation of all actors (policy makers, researchers, public and veterinary health managers, and ecosystem and biodiversity managers), and lead to proactive, rather than reactive approaches to disease control87 (Fig. 3). Particularly, risk maps that integrate multiple layers116 could be produced for surveillance planning and effort monitoring. When pathogen dynamics are poorly understood, adaptive management approaches should be adopted64, in which continuous feedback between modeling and empirical studies, including epidemiological and demographic analyses, allows progressive improvements in the estimation of key parameters, scientific hypothesis testing, and diagnosis.
To conclude, the control of infectious diseases in wildlife is a complex subject for which no magic bullet exists. However, it is crucial to highlight the importance of maintaining long-term data collection and surveillance systems with the objective of monitoring population dynamics, detecting rapidly emergent events, and avoiding endemicity. It is also important to maintain the biodiversity and specific richness of the wild and domestic compartments, two key drivers of resilient socio-ecosystems117.
Lloyd-Smith, J. et al. Epidemic dynamic at the human-animal interface. Science 326, 1362–1367 (2009).
Kilpatrick, A. M. et al. Host heterogeneity dominates West Nile virus transmission. Proc. Biol. Sci. 273, 2327–2333 (2006).
Woolhouse, M., Taylor, L. H. & Haydon, D. T. Population biology of multihost pathogens. Science 292, 1109–1112 (2001).
Cohen., J. Mining coronavirus genomes for clues to the outbreak’s origins. Science https://doi.org/10.1126/science.abb1256 (2020).
D’arc, M. et al. Origin of the HIV-1 group O epidemic in western lowland gorillas. Proc. Natl Acad. Sci. USA 112, E1343–E1352 (2015).
Barrett, A. & Monath, T. Epidemiology and ecology of yellow fever virus. Adv. Virus Res. 61, 291–315 (2003).
Olsen, B. et al. Global patterns of influenza a virus in wild birds. Science 312, 384–388 (2006).
Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–994 (2008).
Kuiken, T. Host Species Barriers to Influenza Virus Infections. Science 312, 394–397 (2006).
Woolhouse, M. et al. Human viruses: discovery and emergence. Philos. Trans. R. Soc. B: Biol. Sci. 367, 2864–2871 (2012).
Myers, N. et al. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
Archer, E.R., Dziba, L.E., Mulongoy, K.J., Maoela, M.A. & Walters, M. (eds). The IPBES regional assessment report on biodiversity and ecosystem services for Africa. 492 (Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 2018).
Jones, B. A. et al. Zoonosis emergence linked to agricultural intensification and environmental change. Proc. Natl Acad. Sci. 110, 8399–8404 (2013).
Hassell, J. M. et al. Urbanization and disease emergence: dynamics at the wildlife–livestock–human interface. Trends Ecol. Evolution 32, 55–67 (2017).
Barroux, G. La santé des animaux et l'émergence d’une médecine vétérinaire au xviiie siècle. Rev. d’histoire des. Sci. 64, 349–376 (2011).
Morens, D. M. et al. Global rinderpest eradication: lessons learned and why humans should celebrate too. J. Infect. Dis. 204, 502–505 (2011).
Anderson, R. M. et al. Population-dynamics of fox rabies in Europe. Nature 289, 765–771 (1981).
Tildesley, M. J. et al. The role of pre-emptive culling in the control of foot-and-mouth disease. Proc. Biol. Sci. 276, 3239–3248 (2009).
Alexander, D. J. An overview of the epidemiology of avian influenza. Vaccine 25, 5637–5644 (2007).
Wang, T., Sun, Y. & Qiu, H.-J. African swine fever: an unprecedented disaster and challenge to China. Infect. Dis. Poverty 7, 111 (2018).
Haydon, D. T., Kao, R. R. & Kitching, R. P. The UK foot-and-mouth disease outbreak—the aftermath. Nat. Rev. Microbiol. 2, 675–U8 (2004).
Andersson, J. A. & Cumming, D. H. in Transfrontier Conservation Areas: People Living on the Edge, (eds Andersson, J. A. et al.). 25–61. (Earthscan: London, 2013).
Cumming, D. H. M., Osofsky S. A., Atkinson S. J. & Atkinson M. W. in One Health: The Theory and Practice of Integrated Health Approaches (eds Zingsstag, J., Schelling, E., Waltner-Toews, D., Whittaker, M. & Tanner, M.) 243–258 (CABI International, 2015).
Aubert, M. F. A. Costs and benefits of rabies control in wildlife in France. Rev. Scientifique et. Tech. de. l’Office Int. des. Epizooties 18, 533–543 (1999).
King, A. A. Historical Perspective of Rabies in Europe and the Mediterranean Basin. A testament to rabies. (eds. King, A.A., Fooks, A. R.., Aubert, M. & Wandeler, A.I.) book is published by the World Organisation for Animal Health (OIE) in conjunction with the World Health Organisation (WHO) Collaborating Centre (2004).
IPBES. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (Advance unedited version 2019).
Bolzoni, L. & G.A. De, Leo Unexpected consequences of culling on the eradication of wildlife diseases: the role of virulence evolution. Am. Naturalist 181, 301–313 (2013).
Streicker, D. G. et al. Ecological and anthropogenic drivers of rabies exposure in vampire bats: implications for transmission and control. Proc. Biol. Sci. 279, 3384–3392 (2012).
Lachish, S. et al. Evaluation of selective culling of infected individuals to control tasmanian devil facial tumor disease. Conserv. Biol. 24, 841–851 (2010).
Dudas, G. et al. MERS-CoV spillover at the camel-human interface. eLife 7, e31257 (2018).
de Garine-Wichatitsky, M. et al. A review of bovine tuberculosis at the wildlife-livestock-human interface in sub-Saharan Africa. Epidemiol. Infect. 141, 1342–1356 (2013).
Johnson, P. T. J. et al. Biodiversity decreases disease through predictable changes in host community competence. Nature 494, 230–230-233 (2013).
Dearing, M. D. et al. The roles of community diversity and contact rates on pathogen prevalence. J. Mammal. 96, 29–36 (2015).
Delahay, R. J. et al. Bovine tuberculosis infection in wild mammals in the South-West region of England: A survey of prevalence and a semi-quantitative assessment of the relative risks to cattle. Vet. J. 173, 287–301 (2007).
Independent Scientific Group on Cattle TB, Bovine TB: The Scientific Evidence. A Science Base for a Sustainable Policy to Control TB in Cattle An Epidemiological Investigation into Bovine Tuberculosis. (2007).
Donnelly, C. & Woodroffe, R. Reduce uncertainty in UK badger culling. Nature 485, 582–582 (2012).
Donnelly, C. et al. Positive and negative effects of widespread badger culling on tuberculosis in cattle. Nature 439, 843–846 (2006).
Donnelly, C. et al. Impact of localized badger culling on tuberculosis incidence in British cattle. Nature 426, 834 (2003).
Hars, J., C. Richomme, & M. L. Boschiroli, Bovine tuberculosis in wild animal in France (Only in French: la tuberculose bovine dans la faune sauvage en France). Bulletin épiémiologique. 38 (Spécial zoonoses, 2010).
Viana, M. et al. Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study. Parasitology 143, 821–834 (2016).
Haydon, D. T. et al. Identifying reservoirs of infection: a conceptual and practical challenge. Emerg. Infect. Dis. 8, 1468–1473 (2002).
Macarthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, Princeton, 2001).
Putman, R. J. Community Ecology (Springer Verlag, New York: Springer Netherlands, 1994).
Odum, E. Fundamentals of Ecology. (Sauders, Washington D.C., 1971.)
Paine, R. T. Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).
Bolzoni, L., Real, L. & De Leo, G. Transmission heterogeneity and control strategies for infectious disease emergence. PLos ONE 2, e747 (2007).
Leroy, E. M. et al. Fruit bats as reservoirs of Ebola virus. Nature 438, 575–576 (2005).
Russell, A. L. et al. Population growth of Mexican free-tailed bats (Tadarida brasiliensis mexicana) predates human agricultural activity. BMC Evolut. Biol. 11, 88–88 (2011).
Donnelly, C. & Woodroffe, R. Bovine tuberculosis: Badger-cull targets unlikely to reduce TB. Nature 526, 640–640 (2015).
Galvani, A. P. & May, R. M. Epidemiology—dimensions of superspreading. Nature 438, 293–295 (2005).
Anderson, R. M. & R. M. May. Infectious Diseases of Humans: Dynamics and Control (eds Anderson R.M. & May, R.M) (Oxford University Press, Oxford, 1991).
Alexander, H. K. & Day, T. Risk factors for the evolutionary emergence of pathogens. J. R. Soc. Interface 8, 1064–1064 (2011).
Paull, S. H. et al. From superspreaders to disease hotspots: linking transmission across hosts and space. Front. Ecol. Environ. 10, 75–82 (2012).
Wolfe, N. D. et al. Naturally acquired simian retrovirus infections in central African hunters. Lancet 363, 932–937 (2004).
Bienen, L. & Tabor, G. Applying an ecosystem approach to brucellosis control: can an old conflict between wildlife and agriculture be successfully managed? Front. Ecol. Environ. 4, 319–327 (2006).
Kramer-Schadt, S., Fernandez, N. & Thulke, H. Potential ecological and epidemiological factors affecting the persistence of classical swine fever in wild boar Sus scrofa populations. Mammal. Rev. 37, 1–20 (2007).
Rands, M. R. W. et al. Biodiversity Conservation: Challenges Beyond 2010. Science 329, 1298–1303 (2010).
Black, F. L. Measles endemicity in insular populations - critical community size and its evolutionary implication. J. Theor. Biol. 11, 207–211 (1966).
Bartlett, M. S. Measles periodicity and community size. J. R. Stat. Soc. Ser. a-Gen. 120, 48–70 (1957).
Delahay, R. J., Smith, G. C., & Hutchings, M. R. Management of Disease in Wild Mammals 284 (Springer, Tokyo, 2009).
Ramsey, D. et al. The effects of reducing population density on contact rates between brushtail possums: implications for transmission of bovine tuberculosis. J. Appl. Ecol. 39, 806–818 (2002).
Begon, M. et al. Transmission dynamics of a zoonotic pathogen within and between wildlife host species. Proc. R. Soc. Lond. Ser. B 266, 1939–1945 (1999).
Lloyd-Smith, J., Getz, W. & Westerhoff, H. Frequency-dependent incidence in models of sexually transmitted diseases: portrayal of pair-based transmission and effects of illness on contact behaviour. Proc. R. Soc. B: Biol. Sci. 271, 625–634 (2004).
Wasserberg, G. et al. Host culling as an adaptive management tool for chronic wasting disease in white-tailed deer: a modelling study. J. Appl. Ecol. 46, 457–466 (2009).
Roche, B. et al. Adaptive evolution and environmental durability jointly structure phylodynamic patterns in avian influenza viruses. PLoS Biol. 12, e1001931 (2014).
Morters, M. K. et al. Evidence-based control of canine rabies: a critical review of population density reduction. J. Anim. Ecol. 82, 6–14 (2013).
Brunker, K. et al. Landscape attributes governing local transmission of an endemic zoonosis: rabies virus in domestic dogs. Mol. Ecol. 27, 773–788 (2018).
Almberg, E. S. et al. Modeling routes of chronic wasting disease transmission: environmental prion persistence promotes deer population decline and extinction. PLoS ONE 6, e19896 (2011).
Dobson, A. & Meagher, M. The population dynamics of brucellosis in the Yellowstone National Park. Ecology 77, 1026–1036 (1996).
Jenkins, H. E., Woodroffe, R. & Donnelly, C. A. The duration of the effects of repeated widespread badger culling on cattle tuberculosis following the cessation of culling. PLoS ONE 5, e9090 (2010).
Donnelly, C. et al. Impacts of widespread badger culling on cattle tuberculosis: concluding analyses from a large-scale field trial. Int. J. Infect. Dis. 11, 300–308 (2007).
Carter, S. P., et al. in Management of Disease in Wild Mammals (eds Delahay, R. J., Smith, G. C. & Hutchings, M. R.) 121–146 (Springer, Tokyo, 2009).
Hallam, T. G. & McCracken, G. F. Management of the panzootic white-nose syndrome through culling of bats. Conserv. Biol. 25, 189–194 (2010).
Carter, S. P. et al. Culling-induced social perturbation in eurasian badgers meles meles and the management of TB in cattle: an analysis of a critical problem in applied ecology. Proc. Biol. Sci. 274, 2769–2777 (2007).
Prentice, J. C. et al. When to kill a cull: factors affecting the success of culling wildlife for disease control. J. R. Soc. Interface 16, 20180901 (2019).
Davidson, R. S. et al. Use of host population reduction to control wildlife infection: rabbits and paratuberculosis. Epidemiol. Infect. 137, 131–138 (2009).
Beeton, N. & McCallum, H. Models predict that culling is not a feasible strategy to prevent extinction of Tasmanian devils from facial tumour disease. J. Appl. Ecol. 48, 1315–1323 (2011).
Horan, R. D. et al. Joint Management of Wildlife and Livestock Disease. Environ. Resour. Econ. 41, 47–70 (2008).
Miguel., E., et al. Optimizing public health strategies in low-income countries: Epidemiology, ecology and evolution for the control of malaria. Ecology and Evolution of Infectious Diseases. 320 (Oxford University Press, Oxford, 2018).
Harrison, A. et al. Culling wildlife hosts to control disease: mountain hares, red grouse and louping ill virus. J. Appl. Ecol. 47, 926–930 (2010).
Bolzoni, L. & De Leo, G.A.. in Environment and Development Economics vol 12: 653–671 (Cambridge University Press, 2007.
Defra, Department for Environment Food and Rural Affairs: Government badger control costs 2017. https://www.gov.uk/government/publications/bovine-tb-government-badger-control-costs/government-badger-control-costs-2017 (Policy paper, 2018).
Defra, National statistics on Bovine TB statistics for Great Britain. (retrieved on from http://www.defra.gov.uk/statistics/foodfarm/landuselivestock/cattletb/) (2017).
Hampton, J. O. & Hyndman, T. H. Underaddressed animal-welfare issues in conservation. Conserv. Biol. 33, 803–811 (2019).
O’Connor, C. M., Haydon, D. T. & Kao, R. R. An ecological and comparative perspective on the control of bovine tuberculosis in Great Britain and the Republic of Ireland. Preventive Vet. Med. 104, 185–197 (2012).
Enticott, G. Public attitudes to badger culling to control bovine tuberculosis in rural Wales. Eur. J. Wildl. Res. 61, 387–398 (2015).
White, P. C. et al. Control of bovine tuberculosis in British livestock: there is no ‘silver bullet’. Trends Microbiol. 16, 420–427 (2008).
Kamath, P. L. et al. Genomics reveals historic and contemporary transmission dynamics of a bacterial disease among wildlife and livestock. Nat. Commun. 7, 11448 (2016).
Lambert., P. Arreté N° DDT-2019-790 authorization for euthanasia of Capra Ibex in massif du Bargy Alps, in Préfet de la Haute-Savoie, D.d.d. territoires, Editor (2019).
Smith, G. C. Models of Mycobacterium bovis in wildlife and cattle. Tuberculosis 81, 51–64 (2001).
Martin, L. E. R. et al. Weather influences trapping success for tuberculosis management in European badgers (Meles meles). Eur. J. Wildl. Res. 63, 30 (2017).
Conner, M. M. et al. A meta-BACI approach for evaluating management intervention on chronic wasting disease in mule deer. Ecol. Appl. 17, 140–153 (2007).
Haley, N. & Richt, J. Evolution of diagnostic tests for chronic wasting disease, a naturally occurring prion disease of cervids. Pathogens 6, 35 (2017).
Bunk, S. Chronic wasting disease—prion disease in the wild. PLos Biol. 2, 427–430 (2004).
Miller, M. W. & Conner, M. M. Epidemiology of chronic wasting disease in free-ranging mule deer: Spatial, temporal, and demographic influences on observed prevalence patterns. J. Wildl. Dis. 41, 275–290 (2005).
ANSES, Control measures for brucellosis in Ibex from Bargy (in French: Mesures de maîtrise de la brucellose chez les bouquetins du Bargy). Edition scientifique, E. scientifique, Editor. (2015).
ANSES, Evaluation de la pertinence de la vaccination des bouquetins du Bargy contre la brucellose. Rapport d’expertise collective (2019).
Choisy, M. & Rohani, P. Harvesting can increase severity of wildlife disease epidemics. Proc. R. Soc. Lond. Ser. B 273, 2025–2034 (2006).
Woodroffe, R. et al. Effects of culling on badger Meles meles spatial organization: implications for the control of bovine tuberculosis. J. Appl. Ecol. 43, 1–10 (2006).
Woodroffe, R. et al. Culling and cattle controls influence tuberculosis risk for badgers. Proc. Natl Acad. Sci. USA 103, 14713–14717 (2006).
Cross, P. C., et al., in Management of Disease in Wild Mammals. (eds Delahay, R., Smith, G. C., Hutchings, M. R.) Chapter 2, 284–284 (Springer, Tokyo, 2009).
Riordan, P. et al. Culling-induced changes in badger (Meles meles) behaviour, social organisation and the epidemiology of bovine tuberculosis. PLoS ONE 6, e28904 (2011).
Courchamp, F., Clutton-Brock, T. & Grenfell, B. Inverse density dependence and the Allee effect. Trends Ecol. Evol. 14, 405–410 (1999).
Lachish, S., McCallum, H. & Jones, M. Demography, disease and the devil: life-history changes in a disease-affected population of Tasmanian devils (Sarcophilus harrisii). J. Anim. Ecol. 78, 427–436 (2009).
Douglass, R. J. et al. Removing deer mice from buildings and the risk for human exposure to Sin Nombre virus. Emerg. Infect. Dis. 9, 390–392 (2003).
Myers, J. H. et al. Eradication revisited: dealing with exotic species. Trends Ecol. Evolution 15, 316–320 (2000).
Woodroffe, R., et al. in Biology and Conservation of Wild Canids. (eds Macdonald, D. W. & Sillero-Zubiri, C.) (Oxford University Press, New York, 2004).
Donnelly, C. et al. Four principles to make evidence synthesis more useful for policy. Nature 558, 361–364 (2018).
Sinclair, A.R., Fryxell, J.M. & Caughley, G. Wildlife Ecology, Conservation and Management (Blackwell Publishing, Oxford, 2006).
Colenutt, C., et al. Environmental sampling as a low-technology method for surveillance of foot-and-mouth disease virus in an area of endemicity. Appl Environ Microbiol. 84, e00686-18 (2018).
Mouchantat, S. et al. Proof of principle: Non-invasive sampling for early detection of foot-and-mouth disease virus infection in wild boar using a rope-in-a-bait sampling technique. Vet. Microbiol. 172, 329–333 (2014).
Bataille, A. et al. Optimization and evaluation of a non-invasive tool for peste des petits ruminants surveillance and control. Sci. Rep. 9, 4742 (2019).
Bitome-Essono, P.-Y. et al. Tracking zoonotic pathogens using blood-sucking flies as ‘flying syringes’. eLife 6, e22069 (2017).
Begon, M., Townsend, C.R. & Harper, J. L. Ecology: From Individuals to Ecosystems. 4th edn, 738 (Blackwell Publishing, Oxford, 2006).
Roche, B. et al. The niche reduction approach: an opportunity for optimal control of infectious diseases in low-income countries? BMC Public Health 14, 753 (2014).
Wint, G. R. et al. Mapping bovine tuberculosis in Great Britain using environmental data. Trends Microbiol. 10, 441–444 (2002).
Mace, G. M. Whose conservation? Changes in the perception and goals of nature conservation require a solid scientific basis. Science 345, 1558–1560 (2014).
Lloyd-Smith, J. et al. Should we expect population thresholds for wildlife disease? Trends Ecol. Evolution 20, 511–519 (2005).
Eberhardt, L. L. Quantitative ecology and impact assessment. J. Environ. Manag. 4, 27–70 (1976).
Tildesley, M. J. et al. Impact of spatial clustering on disease transmission and optimal control. Proc. Natl Acad. Sci. USA 107, 1041–1046 (2010).
Carrington, D. Gassing of badgers considered in plan to eradicate TB in cattle. Available from: https://www.theguardian.com/environment/2013/jul/04/gassing-badgers-eradicate-tb-cattle (2013).
Wobeser, G. Disease management strategies for wildlife. Rev. Scientifique Et. Tech. De. L Off. Int. Des. Epizooties 21, 159–178 (2002).
Bartlett, M. S. The critical community size for measles in the United States. J. R. Stat. Soc. A Stat. 123, 37–44 (1960).
Lefevre, T. & Raymond, M. Biologie évolutive. 2010: Edition de boeck université. Groupe De Boeck s.a. (2010).
Gilbert, L. et al. Disease persistence and apparent competition in a three-host community: an empirical and analytical study of large-scale, wild populations. J. Anim. Ecol. 70, 1053–1061 (2001).
Grenfell, B. & Harwood, J. (Meta)population dynamics of infectious diseases. Trends Ecol. Evolution 12, 395–399 (1997).
McCallum, H., Barlow, N. & Hone, J. How should pathogen transmission be modelled? Trends Ecol. Evolution 16, 295–300 (2001).
Keeling, M. J. et al. Modelling vaccination strategies against foot-and-mouth disease. Nature 421, 136–142 (2003).
May, R. M. & Anderson, R. M. Transmission dynamics of HIV infection. Nature 326, 137–142 (1987).
Antonovics, J., Iwasa, Y. & Hassell, M. P. A generalized model of parasitoid, venereal, and vector-based transmission processes. Am. Naturalist 145, 661–675 (1995).
Clayton, D. A. & Tompkins, D. M. Ectoparasite virulence is linked to mode of transmission. Proc. Biol. Sci. 256, 211–217 (1994).
Pollock, K. H. et al. Large scale wildlife monitoring studies: statistical methods for design and analysis. Environmetrics 13, 105–119 (2002).
Buckland, S. T. et al. in Distance Sampling: Methods and Applications (eds Buckland, S. T. et al.) 29–34 (Springer International Publishing: Cham., 2015).
Miguel, E. et al. Characterising African tick communities at a wild-domestic interface using repeated sampling protocols and models. Acta Tropica 138, 5–14 (2014).
Howe, E. J. et al. Distance sampling with camera traps. Methods Ecol. Evolution 8, 1558–1565 (2017).
Gopal, R. et al. Introduction of bovine tuberculosis to north-east England by bought-in cattle. Vet. Rec. 159, 265–271 (2006).
de la Rua-Domenech, R. et al. Ante mortem diagnosis of tuberculosis in cattle: a review of the tuberculin tests, γ-interferon assay and other ancillary diagnostic techniques. Res. Vet. Sci. 81, 190–210 (2006).
Gilbert, M. et al. Cattle movements and bovine tuberculosis in Great Britain. Nature 435, 491–496 (2005).
Brooks-Pollock, E., Roberts, G.O. & Keeling, M.J. A dynamic model of bovine tuberculosis spread and control in Great Britain. Nature 511: 228–231 (2014)
Defra, Bovine Tuberculosis Evidence Plan. Department for Environment Food and Rural Affairs. (National Audit, 2013).
Vial, F. et al. Bovine tuberculosis risk factors for british herds before and after the 2001 foot-and-mouth epidemic: what have we learned from the TB99 and CCS2005 Studies? Transbound. Emerg. Dis. 62, 505–515 (2015).
Menzies, F. D. & Neill, S. D. Cattle-to-cattle transmission of bovine tuberculosis. Vet. J. 160, 92–106 (2000).
Donnelly, C.A. & Nouvellet, P. The contribution of badgers to confirmed tuberculosis in cattle in high-incidence areas in England. PLoS Curr. https://currents.plos.org/outbreaks/index.html%3Fp=22371.html (2013).
Corner, L. A. L., Murphy, D. & Gormley, E. Mycobacterium bovis infection in the Eurasian badger (Meles meles): the disease, pathogenesis, epidemiology and control. J. Comp. Pathol. 144, 1–24 (2011).
Defra. Bovine TB: authorisation for badger control in 2017. https://www.gov.uk/government/publications/bovine-tb-authorisation-for-badger-control-in-2017 (2017).
Aguilar, X. F. et al. PPR virus threatens wildlife conservation. Science 362, 165 (2018).
Hamede, R. et al. Reduced effect of tasmanian devil facial tumor disease at the disease front. Conserv. Biol. 26, 124–134 (2012).
Lachish, S., Jones, M. & McCallum, H. The impact of disease on the survival and population growth rate of the Tasmanian devil. J. Anim. Ecol. 76, 926–936 (2007).
Hawkins, C. E. et al. Emerging disease and population decline of an island endemic, the Tasmanian devil Sarcophilus harrisii. Biol. Conserv. 131, 307–324 (2006).
Rosatte, R. et al. The elimination of raccoon rabies from Wolfe Island, Ontario: Animal density and movements. J. Wildl. Dis. 43, 242–250 (2007).
Haydon, D. T. et al. Low-coverage vaccination strategies for the conservation of endangered species. Nature 443, 692–695 (2006).
Ramsey, D. S. L. & Efford, M. G. Management of bovine tuberculosis in brushtail possums in New Zealand: predictions from a spatially explicit, individual-based model. J. Appl. Ecol. 47, 911–919 (2010).
Tompkins, D. M. et al. Oral vaccination reduces the incidence of tuberculosis in free-living brushtail possums. Proc. Biol. Sci. 276, 2987–2995 (2009).
Brochier, B. et al. Large-scale eradication of rabies using recombinant vaccina rabies vaccines. Nature 354, 520–522 (1991).
Ballesteros, C. et al. Acceptance and palatability for domestic and wildlife hosts of baits designed to deliver a tuberculosis vaccine to wild boar piglets. Preventive Vet. Med. 98, 198–203 (2011).
Rossi, S. et al. New insights on the management of wildlife diseases using multi-state recapture models: the case of classical swine fever in wild boar. PLoS ONE 6, e24257 (2011).
Kaden, V. et al. Oral immunisation of wild boar against classical swine fever: evaluation of the first field study in Germany. Vet. Microbiol. 73, 239–252 (2000).
Wilkinson, D. et al. A model of bovine tuberculosis in the badger Meles meles: An evaluation of different vaccination strategies. J. Appl. Ecol. 41, 492–501 (2004).
Foggin, C. in Fencing Impacts: A review of the environmental, social and economic impacts of game and veterinary fencing in Africa with particular reference to the Great Limpopo and Kavango-Zambezi transfrontier Conservation Areas (eds Ferguson, K. & Hamks, J) (University of Pretoria: Pretoria, 2010)
Smith, G. C. & Cheeseman, C. L. A mathematical model for the control of diseases in wildlife populations: culling, vaccination and fertility control. Ecol. Model. 150, 45–53 (2002).
Lindsey., P. A. et al. in Fencing for Conservation: Restriction of Evolutionary Potential or a Riposte to Threatening Processes? Chapter 12(eds Somers, M. J. & Hayward, M. W.) (Springer Science+Business Media, LLC, 2012).
Pepperell, C. S. et al. Dispersal of Mycobacterium tuberculosis via the Canadian fur trade. Proc. Natl Acad. Sci. USA 108, 6526–6531 (2011).
Livingstone, P. G. et al. Regionalization: a strategy that will assist with bovine tuberculosis control and facilitate trade. Vet. Microbiol. 112, 291–301 (2006).
Animal and Plant Health Agency. Bovine TB testing intervals (2017).
Packer, C. et al. Keeping the herds healthy and alert: implications of predator control for infectious disease. Ecol. Lett. 6, 797–802 (2003).
Duffy, M. A. et al. Unhealthy herds: indirect effects of predators enhance two drivers of disease spread. Funct. Ecol. 25, 945–953 (2011).
Proffitt, K. M., White, P. J. & Garrott, R. A. Spatio-temporal overlap between Yellowstone bison and elk— implications of wolf restoration and other factors for brucellosis transmission risk. J. Appl. Ecol. 47, 281–289 (2010).
Wild, M. A. et al. The role of predation in disease control: a comparison of selective and nonselective removal on prion disease dynamics in deer. J. Wildl. Dis. 47, 78–93 (2011).
Miguel, E. et al. Drivers of Foot and Mouth Disease in cattle at wild/domestic interface: insights from farmers, buffalo and lions. Diversity Distrib. 23, 1018–1030 (2017).
Hosseini, P. R., et al. Does the impact of biodiversity differ between emerging and endemic pathogens? The need to separate the concepts of hazard and risk. Philosophical Transactions of the Royal Society B: Biological Sciences 372, 1722 (2017).
FAO, The second report on the state of the world’s animal genetic resources for food and agriculture (F.c.o.g.r.f.f.a.a. assessments, 2015).
Clifton-Hadley, R. S., Wilesmith, J. W. & Stuart, F. A. Mycobacterium bovis in the European badger (Meles meles): epidemiological findings in tuberculous badgers from a naturally infected population. Epidemiol Infect.111, 9–19 (1993).
Olea-Popelka, F. J. et al. Targeted badger removal and the subsequent risk of bovine tuberculosis in cattle herds in county Laois, Ireland. Preventive Vet. Med. 88, 178–184 (2009).
Griffin, J. M. et al. The impact of badger removal on the control of tuberculosis in cattle herds in Ireland. Preventive Vet. Med. 67, 237–266 (2005).
le Roex, N., et al. Disease control in wildlife: evaluating a test and cull programme for bovine tuberculosis in African buffalo. Transbound Emerg. Dis. 63, 647–657 (2015).
Radunz, B. Surveillance and risk management during the latter stages of eradication: experiences from Australia. Vet. Microbiol. 112, 283–290 (2006).
O’Brien, D. J. et al. Recent advances in the management of bovine tuberculosis in free-ranging wildlife. Vet. Microbiol. 151, 22–33 (2011).
Livingstone, P. G., et al. Development of the New Zealand strategy for local eradication of tuberculosis from wildlife and livestock. N. Z. Vet. J. 63, 98–107 (2015)..
Boadella, M. et al. Effects of culling Eurasian wild boar on the prevalence of Mycobacterium bovis and Aujeszky’s disease virus. Preventive Vet. Med. 107, 214–221 (2012).
García-Jiménez, W. L. et al. Reducing Eurasian wild boar (Sus scrofa) population density as a measure for bovine tuberculosis control: effects in wild boar and a sympatric fallow deer (Dama dama) population in Central Spain. Preventive Vet. Med. 110, 435–446 (2013).
Carstensen, M. & DonCarlos, M. W. Preventing the establishment of a wildlife disease reservoir: a case study of bovine tuberculosis in wild deer in minnesota, USA. Vet. Med. Int. 2011, 10 (2011).
Cosgrove, M. K., et al. Live-trapping and bovine tuberculosis testing of free-ranging white-tailed deer for targeted removal. Wildlife Res. 39, 104–111 (2012).
We acknowledge Pierre Nouvellet and Flavie Vial for their fruitful comments on the paper.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Miguel, E., Grosbois, V., Caron, A. et al. A systemic approach to assess the potential and risks of wildlife culling for infectious disease control. Commun Biol 3, 353 (2020). https://doi.org/10.1038/s42003-020-1032-z