Multi-event capture–recapture modeling of host–pathogen dynamics among European rabbit populations exposed to myxoma and Rabbit Hemorrhagic Disease Viruses: common and heterogeneous patterns
- Simone Santoro†1,
- Isa Pacios†2,
- Sacramento Moreno2,
- Alejandro Bertó-Moran2 and
- Carlos Rouco2, 3Email author
© Santoro et al.; licensee BioMed Central Ltd. 2014
Received: 12 September 2013
Accepted: 17 March 2014
Published: 5 April 2014
Host–pathogen epidemiological processes are often unclear due both to their complexity and over-simplistic approaches used to quantify them. We applied a multi-event capture–recapture procedure on two years of data from three rabbit populations to test hypotheses about the effects on survival of, and the dynamics of host immunity to, both myxoma virus and Rabbit Hemorrhagic Disease Virus (MV and RHDV). Although the populations shared the same climatic and management conditions, MV and RHDV dynamics varied greatly among them; MV and RHDV seroprevalences were positively related to density in one population, but RHDV seroprevalence was negatively related to density in another. In addition, (i) juvenile survival was most often negatively related to seropositivity, (ii) RHDV seropositives never had considerably higher survival, and (iii) seroconversion to seropositivity was more likely than the reverse. We suggest seropositivity affects survival depending on trade-offs among antibody protection, immunosuppression and virus lethality. Negative effects of seropositivity might be greater on juveniles due to their immature immune system. Also, while RHDV directly affects survival through the hemorrhagic syndrome, MV lack of direct lethal effects means that interactions influencing survival are likely to be more complex. Multi-event modeling allowed us to quantify patterns of host–pathogen dynamics otherwise difficult to discern. Such an approach offers a promising tool to shed light on causative mechanisms.
Emerging and re-emerging infectious diseases present one of the most pressing issues facing wild vertebrate populations in the 21st century . However, relatively little is yet known about the exposure dynamics of infectious agents in their individual hosts, and what determines their impact on life-history traits such as survival . A comprehensive understanding of the ecological processes influencing pathogen dynamics in natural host populations is of crucial importance to predicting both the dynamics of infectious diseases and the risks that they may entail for animal populations [3, 4].
The European wild rabbit (Oryctolagus cuniculus) and the two main viral diseases that affect them (myxomatosis, and Rabbit Hemorrhagic Disease) represent an important system for addressing wildlife eco-epidemiological issues. European rabbits are a multifunctional keystone species; not only do they alter plant species composition and vegetation structure, but they also represent the bulk of the diet of a wide variety of Iberian predators . These include the Iberian lynx (Lynx pardinus) and the Spanish imperial eagle (Aquila adalberti), both seriously threatened [5–7]. The arrival of myxoma virus (MV) and Rabbit Hemorrhagic Disease Virus (RHDV) on the Iberian Peninsula has caused a marked decline in rabbit populations over the last 50 years [8–11], threatening further population losses among their predator species [12, 13]. In an attempt to prevent local extinction in its native range, and to avoid predator co-extinctions, the European rabbit has been made a conservation priority in both Spain and Portugal [14–17]. In addition the loss of rabbits, considered a pest species across most of their introduced range , has in some areas of the Iberian Peninsula caused large-scale economic loss and environmental degradation (e.g. [19, 20]).
In spite of the ecological and economic relevance of these issues, disease surveillance still broadly uses simple counts of infected and uninfected animals, although more accurate statistical tools that account for imperfect detection are now available (see  for a review of capture–recapture modeling in epidemiology). A few studies have used simple Cormack–Jolly–Seber (CJS) capture–recapture models to investigate MV and RHDV effects on rabbit population dynamics (e.g. [22, 23]). As an extension of single-state (CJS) models, multi-state capture–recapture models allow the direct estimation and testing of hypotheses about seroconversion rates and state-specific survival rates [24–26]. However, assessment of the infectious status of all captured individuals is often unfeasible, and some data rearrangement (e.g. data-censoring) is usually required for the application of multi-state models (reviewed in ). To avoid these limitations, multi-event capture–recapture models have been recently developed  allowing uncertainty in state assessment to be modeled. Here we apply this novel approach to test hypotheses about the effects on survival of, and the dynamics of host immunity to, both MV and RHDV (which occur naturally in the study area). As a general prediction, based on previous studies, we expected MV and RHDV seropositives to have higher survival rates than seronegatives (e.g. [29–31]). In addition, we aimed to do the following: (i) estimate monthly survival rates dependent on antibody status, age (juveniles vs. adults) and sex, (ii) assess monthly seroconversion rates (from seropositive to seronegative, and vice versa) with respect to both diseases according to hosts’ age and sex, (iii) quantify the dynamics of seroprevalence to each virus over time, expressed as the seropositive probability, and (iv) examine relationships between population size and seroprevalence.
Materials and methods
All animal manipulations reported in this paper were made in accordance with Spanish and European regulations (Law 32/2007, R.D. 1201/2005 and Council Directive 2010/63/EU).
Study area and sampling
The study was carried out in the southwestern Iberian Peninsula (Hornachuelos Natural Park; 37°49′ N, 5°15′ W; 100–700 m elevational range), where the climate is Mediterranean with hot, dry summers and cool, wet winters . A rabbit breeding program was implemented in the area in 2008 for conservation purposes; the main objective was to increase rabbit abundance in the area to enhance survival of threatened predators. Three enclosures (E1, E2, E3; about 4 ha each) were built as breeding zones for rabbits, being surrounded by 2.5-m-high chain-link fence to both prevent rabbit dispersal and exclude terrestrial predators . Each enclosure contained 30 regularly distributed artificial warrens, water and pellet food were supplied ad libitum, and grass was sown to increase the availability of fresh food.
From May 2008 to April 2010, nine one-day live-trapping sessions were carried out at each enclosure. Time-intervals between capture sessions varied slightly across enclosures and through the time period; on average a capture session was performed every 2.9 months (SE: 0.18). Live rabbits were caught in cage traps placed surrounding each warren as described in , with 50–60% of rabbits from each warren caught at each session . Captured animals were handled at the trap-site, being marked with a numbered ear tag and having their sex and weight recorded. Females and males weighing more than 750 g and 850 g respectively were considered adults [36–38] (see Additional file 1 for details on sex and age population structures). Blood samples (1–2.5 mL) were collected from 1125 (17%) of 6605 captured animals over the course of the study. Details of the serology protocols employed are given in . For both viruses, we considered seropositive those individuals with antibody concentrations sufficient to confer protection against disease as resulting from the ELISA test Information brochure (Ingezim Rabbit 1.7., Ingenasa Laboratory, Madrid, Spain). The probability of either false-positive or false-negative diagnoses under this paradigm are negligible (P < 0.02; ) making state misclassification unnecessary to be modeled. Furthermore, since antibodies against both viral diseases are detectable in blood for a short time (8–13 days) after exposure to either pathogen , seroprevalence to MV or RHDV provides a reliable insight into the epidemiological dynamics of the two diseases.
Multi-event capture–recapture analyses
Capture–recapture sessions were not synchronized among the three fenced areas (E1–E3), for logistic reasons. As a result, we ran separate analyses for each enclosure and disease agent (MV and RHDV). With these populations serving as breeding zones for restocking purposes, random samples of captured individuals were periodically removed during capture sessions (removals of individuals is coded in the data sets and does not affect estimation of parameters). Sample sizes for capture–recapture analyses (i.e. number of captures minus number of removals) were 2573, 1771 and 1400 for E1–E3 respectively. Numbers of animals captured per trapping session, divided by the surface of the trapped enclosure, were used as indices of rabbit density  for each capture session and enclosure.
Goodness of fit
Since no goodness of fit (GOF) is available for multi-event models, for each population we used U-CARE 2.3.2  to test the fit of the Cormack–Jolly–Seber (CJS) model that accounted only for time variation in survival and capture probabilities. The CJS model is therefore more restrictive than those fitted for testing hypotheses that do account for the effect of serological status on recapture and transition probabilities. Hence, this approach is conservative given that if the CJS model adequately fits the data, then the multi-event models are also expected to fit. For each population/GOF analysis, we defined four groups according to age at first capture (adult vs. juvenile) and gender. U-CARE allows testing for specific lack of fit due to a transience effect (i.e. a higher than expected presence of individuals showing up only once; test component 3.SR) and/or to trap-dependence (i.e. capture probability depending on the fact they were captured or not; test component 2.CT). When the overall (all the groups and components together) goodness-of-fit tests were significant, sources of extra-binomial variation were accounted for in the multi-event global models by including transience and/or trap dependence (according to the output of tests 3.SR and 2.CT on each specific group). Over-dispersion factors (c-hat) were then calculated as the ratio between the sum of χ2 values and degrees of freedom of the non-significant test components .
For E1, the global goodness-of-fit test indicated lack of fit of the CJS model (χ2 = 112.47; d.f. = 87; P = 0.034), detecting “trap-happiness” among both adult males and females (P = 0.01 and < 0.01 respectively). For E2 and E3, there was no evidence of lack of fit (χ2 = 73.53; d.f. = 88; P = 0.87, and χ2 = 73.53; d.f. = 88; P = 0.8, respectively). We thus modeled trap-dependence among adults for E1 (see  and Additional file 2 for details on the probabilistic framework used). Correction for over-dispersion (c-hat = 1) was not needed for any analysis. Both survival and seroconversion rates have probably varied throughout the study period. However, because of limited sample sizes, and because our primary interest was in the net immunological effects on rabbit dynamics, we chose not to include a time effect on Survival and Seroconversion parameters.
During each field session (excluding the fifth, for logistical reasons) a variable and random sample of captured individuals were blood-sampled (regardless of sex, age, and encounter history) and their MV and RHDV immunological statuses (seropositive or seronegative) were assessed. To account for uncertainty in state-assessment when an individual is not bled (sensu “partial observation”; ) we used multi-event capture–recapture models  in E-SURGE 1.8.5 . Unlike traditional methods for handling partial information on states, like data censoring or the extra-state approach , capture records in the multi-event framework are defined as events (i.e. reflecting the way the underlying biological states are observed in the field). It is therefore possible to define a specific event to record the capture of an individual whose state is unknown. Here we considered four events (not seen, 0; seen, bled and assessed as seronegative, 1; seen, bled and assessed as seropositive, 2; seen and not bled, 3) and three possible states (dead, †; alive seronegative, SN; alive seropositive, SP). A slightly different set of states was used in models accounting for trap-dependence (details on the probabilistic framework are given in Additional file 2).
Multi-event models include three parameter types, Initial State (related to the probability of being in some specific state when first captured), Transition (related to the probability of transition between states) and Event (related to the probabilities of being re-sighted according to the event-mediated information on states). We decomposed Transition into two steps: Survival (the survival probability) and, conditional on still being alive, Seroconversion (the seroconversion probability). Event was decomposed into two steps: Capture (accounting for recapture probability) and, conditional on being captured, State Assignment (accounting for the probability the immunological status was assessed). In this study, Initial State estimates the probability one first-captured individual is seropositive. Therefore, by assuming that the probability of first captured individuals being seropostive reflects the percentage of seropositive individuals in the population (but see [44, 45] for a discussion on this), Initial State can be a proxy for the seroprevalence in each population.
We ran six analyses, one for each population–disease combination. We used QAICc values , to test for effects of immunity, age and sex on both rabbit survival and seroconversion rates (from seropositive to seronegative, and vice versa). Since populations were closed to immigration and emigration, survival rates referred to real survival rates . We assumed that time intervals were short enough that multiple transitions between serological states were unlikely to occur between two consecutive sessions and no bias was expected on seroconversion estimates . In E-SURGE we marked the “uneven time intervals” option to allow monthly estimates of both survival and seroconversion probabilities even though intervals between capture sessions were not on a monthly basis. We also used the best model from each analysis to test the effect of population density on Initial State (seroprevalence). For each analysis, we computed the significance and percentage of Initial State variation explained by density using analysis of deviance (ANODEV) . This procedure compares the deviance and number of estimable parameters of three models identical except for the parameter of interest (Initial State in this case) which is: (i) constant, (ii) full-time dependent, or (iii) dependent on density.
Based on preliminary model exploration, Initial State and State Assignment depended on, respectively, time and time-by-immunological status and were not further modeled. The other parameters of the global model accounted for these effects: (i) Survival on age-by-sex-by-immunological status, (ii) Seroconversion on sex-by-immunological status; Capture on sex plus age-by-immunological status-by-time (in E1 also on trap-response).
For each population–disease combination, we first modeled recapture probabilities. The structure for recapture probabilities was then fixed as per the model with the lowest QAICc value, and Survival and Seroconversion probabilities were modeled independently. While we modeled Survival we kept the most parameterized structure for Seroconversion, and vice versa. For each parameter we considered a set of candidate models made of models nested to the global model. To keep the number of tested models as low as possible , we only considered interactive effects for parameters whose time-variation was not modeled (i.e. Survival and Seroconversion).
A final set of models combined the best structures for both Survival and Seroconversion (lowest QAICc when modeled independently) (, for a similar approach). Hence we used this set of models to compute, for each parameter, model-averaged estimates from models lying within 2 Δ of the best model .
Myxoma virus and survival
Myxoma Virus model-selection best models
np; Dev; QAICc; wi
58; 6694.55; 6813.05; 0.66
56; 6700.78; 6815.10; 0.23
58; 6699.49; 6817.99; 0.06
62; 6691.23; 6818.08; 0.92
53; 4733.87; 4053.88; 0.88
49; 4750.48; 4059.29; 0.06
53; 4733.87; 4053.88; 1
48; 3664.89; 3764.20; 0.69
54; 3654.65; 3766.85; 0.18
50; 3664.83; 3768.43; 0.08
52; 3656.86; 3764.76; 0.71
54; 3654.65; 3766.85; 0.25
Rabbit hemorrhagic disease and survival
Rabbit Hemorrhagic Disease Virus model-selection best models
np; Dev; QAICc; wi
83; 6518.66; 6689.61; 0.73
79; 6530.35; 6692.83; 0.15
77; 6535.99; 6694.24; 0.07
81; 6519.89; 6686.59; 0.74
83; 6518.66; 6689.61; 0.16
49; 4724.47; 4037.61; 0.62
53; 4718.07; 4040.71; 0.13
47; 4733.35; 4040.81; 0.13
49; 4729.07; 4041.44; 0.09
53; 4718.07; 4040.71; 0.82
51; 4726.87; 4043.82; 0.17
42; 3830.64; 3917.17; 0.42
40; 3835.32; 3917.62; 0.34
40; 3837.85; 3920.15; 0.09
42; 3834.10; 3920.63; 0.07
46; 3827.61; 3922.65; 0.54
44; 3832.35; 3923.13; 0.43
Myxoma virus seroconversion
Rabbit Hemorrhagic Disease Virus seroconversion
We found very different patterns of RHDV seroconversion among enclosures (Table 2, Figure 2, and Additional file 4). In E1, no difference was observed among genders, with all rabbits becoming seropositive at a faster rate than the reverse. In E2, there were no consistent differences in seroconversion rates of males, while females became seropositive at a higher rate than the reverse (and faster than males). In contrast, in E3 both sexes became seropositive at an unusually high rate, and faster than they became seronegative (from model without sex effect: respectively, 0.72, SE: 0.05; 0.14, SE: 0.02).
Seroprevalence and density
Effect of rabbit density on Initial State (seroprevalence of MV and RHDV)
Myxo – Prev
RHD – Prev
4.65; 0.07; 0.66
5.11; 0.06; 0.73
0.25; 0.63; 0.03
0.80; 0.4; 0.11
5.01; 0.06; 0.71
2.94; 0.13; 0.42
Population size in the three enclosures followed similar patterns, being lower during the winter and higher during the spring. The three populations reached maximum peak densities at the same time in May 2010, while minimum densities occurred at different times. Average rabbit densities (no./hectare) per enclosure (± 95% CI) over the whole study period were 79.6 ± 53.1, 49.9 ± 31.4, and 52.5 ± 38.5 (for E1–E3 respectively). We observed a marginally significant positive relationship between rabbit density and MV seroprevalence for E1 (slope on logit scale: 1.73; SE: 0.3; P: 0.07; 66% variation explained), and a negative such relationship for E3 (slope on logit scale: -1.13; SE: 0.26; P: 0.06; 71% variation explained). We also observed a marginally significant positive relationship between density and RHDV seroprevalence in E1 (slope on logit scale: 1.41; SE: 0.17; P: 0.06; 73% variation explained).
Recapture rates varied through time in all analyses (average estimates ± SD from best models of MV analyses: E1, 0.61 ± 0.22; E2, 0.48 ± 0.19; E3, 0.28 ± 0.19). We found either sex, age, or immunological status affected the probability of being recaptured (in E1 trap happiness was also confirmed throughout model selection). However, very heterogeneous causal effects on recapture probabilities were found among and within the rabbit populations.
Our study confirms that rabbit populations respond very differently in terms of host survival and epidemiological dynamics when exposed to MV and RHDV, even if geographically close to each other [51, 52]. Multiple interacting causes likely explain this heterogeneity. However, some general patterns were observed in our study: (i) RHDV seropositivity was never related to increased rabbit survival; (ii) seropositivity to both MV and RHDV was negatively related to survival more frequently for juveniles than for adults; and (iii) once MV seropositive, rabbits rarely lost such status.
Our results do not conform to the overall published pattern of MV and RHDV seropositive rabbits always having higher survival rates than seronegatives [29–31]. Only in one case, MV seropositives had considerably higher survival rates than seronegatives. However, it should be noted that our populations were in semi-natural captive conditions allowing high densities, which greatly exceeded wild population densities (e.g. c. 3 rabbits/ha for the central Iberian Peninsula; ). Therefore our results could be extrapolated to high density scenarios in the wild (e.g. [54, 55]).
Seropositivity to MV and RHDV could potentially confer both advantages and disadvantages to rabbits. While it may confer higher immunity to these pathogens, several authors have recognized that (for MV at least) seropositivity has an immunosuppressive effect that may cause higher rates of co-infection with other pathogens [56–58]. With the target organ of RHDV (the liver) being part of the immune system, RHDV may also cause an immunosuppressive syndrome in addition to reduced survival due to hemorrhagic effects [59–61]. Rabbits seropositive to either virus are thus likely to be more susceptible to damage from other infections. A shifting balance of advantages and disadvantages is thus one explanation for contrasting patterns, and is likely influenced by interactions with other environmental, epidemiologic and individual factors. For example, in our population E3 rabbits were in a better physiological status than in the others as a result of availability of higher water quality (e.g. in streams, manuscript in preparation). We suggest that this reduced the negative effect of the MV-seropositive-related immunosuppression, explaining why they had higher survival rates than seronegatives. As another example, RHDV seropositivity never being associated with higher survival rates could indicate that the negative effects of the hemorrhagic syndrome outweighed any beneficial effects. Finally, seropositive juveniles tending to survive less than seronegatives may be explained by the immaturity of the juvenile immune system with full immunity not yet being fully acquired. In light of this, it is possible that negative influences such as immunosuppression and the hemorrhagic syndrome of RHDV had greater influence on juveniles than adults.
With ongoing persistence 50 and 30 years after the arrival of myxomatosis and RHD respectively on the Iberian Peninsula , it is reasonable to classify the viruses as endemic. Accordingly, rabbits should gain MV and RHD antibodies as fast as or faster than they lose them. Seroconversion to seropositive status tending to occur at a faster rate than the reverse in our study (and also  and ) supports this expectation. However, this was not the case for RHDV in E3, where seroconversion to seronegative status occurred at a higher rate. We suggest this result does not disprove the endemic disease behavior hypothesized above, but is driven by asynchrony between population and virus dynamics. In fact, RHDV was found in this population over the 7 years of monitoring (SM, unpublished data), but the population had a delayed breeding season that, differently from in the other enclosures, occurred after the RHD outbreak. Thus, in E3 the naïve kittens would have lost their maternal antibodies after 2 months  and, since they were not exposed to RHDV, a large number of seronegative adults (many of them born the spring just before) would have occurred in the next autumn. This would result in a large number of seronegatives over the study period and explain this seemingly inconsistent result. It should also be noted that, in contrast to general belief , the average monthly probability of losing antibodies was not necessarily null for either RHDV or MV. This indicates that rabbits can lose immunity to these diseases (albeit with a very low probability). The effect of sex on the rate at which individuals became seropositive varying among populations further illustrates the varying behavior of these diseases across individuals and populations [51, 52].
Antibody prevalence and density
In general, seroprevalence did not follow a consistent pattern either within or among populations for either disease. This was in agreement with previous findings from some authors, stating these diseases behave very differently among populations [51, 52], but contrasts with a study in the Canary Islands  where they found no difference in RHDV prevalence across four neighboring geographic zones.
Host infection by both viruses also occurs by means of contact with an infected individual. Population size is thus recognized as an important factor promoting MV and RHDV dynamics [52, 66–68]. However, even though in some cases a great amount of variation in seroprevalence appeared to be explained by density, in no case did we find this hypothesis was strongly supported (P-values were marginally significant at the 0.05 level). The pattern of increasing prevalence with population size observed in E1 for both MV and RHDV was unsurprising. However, we observed no such relationships in E2 and a negative relationship between MV prevalence and population size in E3. This last result may have been caused by a correlation between decreasing density due to gradual habitat degradation (authors personal observation) leading to individuals under nutritional stress being more susceptible to infection.
This is the first multi-event study focusing on MV and RHDV host–pathogen dynamics in rabbit populations. We found that while MV seropositivity had either a positive or negative effect on survival that was likely dependent on interaction with other factors (e.g. physiological condition), the hemorrhagic syndrome caused by RHDV led seropositive rabbits to suffer higher mortality rates. Our study highlights that the host–pathogen dynamics of these viruses are highly variable among populations even when these share similar management and climatic conditions. These findings have important implications for rabbit population management, particularly where their scarcity could compromise ecosystem conservation. Additional well-defined capture–recapture analyses may shed further light on the still many obscure mechanisms driving host–pathogen dynamics (e.g. [69, 70]).
Special thanks go to V. Morlanes, C. Marfil, G. Macıas, and J. Garcıa for support during fieldwork. We also thank Dr Daniel Tompkins (Landcare Research, New Zealand) for helpful comments and stimulating criticism on a previous draft of the manuscript. Christine Bezar of Landcare Research provided valuable editorial comments. Funding was provided by an Ingenierıa de Protección Ambiental-Consejo Superior de Investigaciones Científicas (IPA-CSIC) contract through projects 20070687_5 and 20091418. The work reported in this paper complies with standards and procedures promulgated by Spanish legislation.
- Dobson AP, Foufopoulos J: Emerging infectious pathogens of wildlife. Phil Trans R Soc B. 2001, 356: 1001-1012. 10.1098/rstb.2001.0900.PubMed CentralView ArticlePubMedGoogle Scholar
- Benskin CMH, Wilson K, Jones K, Hartley IR: Bacterial pathogens in wild birds: a review of the frequency and effects of infection. Biol Rev Camb Philos Soc. 2009, 84: 349-373. 10.1111/j.1469-185X.2008.00076.x.View ArticlePubMedGoogle Scholar
- Daszak P, Cunningham AA, Hyatt AD: Emerging infectious diseases of wildlife - threats to biodiversity and human health. Science. 2000, 287: 443-449. 10.1126/science.287.5452.443.View ArticlePubMedGoogle Scholar
- Chambert T, Staszewski V, Lobato E, Choquet R, Carrie C, McCoy KD, Tveraa T, Boulinier T: Exposure of black-legged kittiwakes to Lyme disease spirochetes: dynamics of the immune status of adult hosts and effects on their survival. J Anim Ecol. 2012, 81: 986-995. 10.1111/j.1365-2656.2012.01979.x.View ArticlePubMedGoogle Scholar
- Delibes-Mateos M, Delibes M, Ferreras P, Villafuerte R: Key role of European rabbits in the conservation of the Western Mediterranean basin hotspot. Conserv Biol. 2008, 22: 1106-1117. 10.1111/j.1523-1739.2008.00993.x.View ArticlePubMedGoogle Scholar
- Ferrer M, Negro JJ: The near extinction of two large European predators: super specialists pay a price. Conserv Biol. 2004, 18: 344-349. 10.1111/j.1523-1739.2004.00096.x.View ArticleGoogle Scholar
- Delibes-Mateos M, Ferreras P, Villafuerte R: European rabbit population trends and associated factors: a review of the situation in the Iberian Peninsula. Mamm Rev. 2009, 39: 124-140. 10.1111/j.1365-2907.2009.00140.x.View ArticleGoogle Scholar
- Calvete C, Pelayo E, Sampietro J: Habitat factors related to wild rabbit population trends after the initial impact of rabbit haemorrhagic disease. Wildl Res. 2006, 33: 467-474. 10.1071/WR05107.View ArticleGoogle Scholar
- Delibes-Mateos M, Redpath SM, Angulo E, Ferreras P, Villafuerte R: Rabbits as a keystone species in southern Europe. Biol Conserv. 2007, 137: 149-156. 10.1016/j.biocon.2007.01.024.View ArticleGoogle Scholar
- Delibes-Mateos M, Farfán MA, Olivero J, Vargas J: Land-use changes as a critical factor for long-term wild rabbit conservation in the Iberian Peninsula. Environ Conserv. 2010, 37: 169-176. 10.1017/S0376892910000214.View ArticleGoogle Scholar
- García-Bocanegra I, Astorga RJ, Napp S, Huerta B, Carbonero A, Perea A, Arenas A: Factors affecting the seroprevalence of lagovirus infection in wild rabbits (Oryctolagus cuniculus) in Southern Spain. Vet J. 2011, 189: 89-94. 10.1016/j.tvjl.2010.06.009.View ArticlePubMedGoogle Scholar
- Moreno S, Villafuerte R, Cabezas S, Lombardi L: Wild rabbit restocking for predator conservation in Spain. Biol Conserv. 2004, 118: 183-193. 10.1016/j.biocon.2003.07.020.View ArticleGoogle Scholar
- Moleon M, Sanchez-Zapata JA, Real J, García-Charton JA, Gil-Sánchez JM, Palma L, Bautista J, Bayle J: Large-scale spatio-temporal shifts in the diet of a predator mediated by an emerging infectious disease of its main prey. J Biogeogr. 2009, 36: 1502-1515. 10.1111/j.1365-2699.2009.02078.x.View ArticleGoogle Scholar
- Rogers PM: Predator–prey relationship between rabbit and lynx in southern Spain. Terre Vie. 1978, 32: 83-87.Google Scholar
- Moreno S, Villafuerte R: Traditional management of scrubland for the conservation of rabbits Oryctolagus cuniculus and their predators in Doñana National Park, Spain. Biol Conserv. 1995, 73: 81-85. 10.1016/0006-3207(95)90069-1.View ArticleGoogle Scholar
- Palomares F, Gaona P, Ferreras P, Delibes M: Positive effects on game species of top predators by controlling smaller predator populations: an example with lynx, mongoose and rabbits. Conserv Biol. 1995, 9: 295-305. 10.1046/j.1523-1739.1995.9020295.x.View ArticleGoogle Scholar
- Travaini A, Delibes M, Ferreras P, Palomares F: Diversity, abundance or rare species as a target for the conservation of mammalian carnivores: a case study in southern Spain. Biodivers Conserv. 1997, 6: 529-535. 10.1023/A:1018329127772.View ArticleGoogle Scholar
- Lees AC, Bell DJ: A conservation paradox for the 21st century: the European wild rabbit Oryctolagus cuniculus, an invasive alien and an endangered native species. Mamm Rev. 2008, 38: 304-320. 10.1111/j.1365-2907.2008.00116.x.View ArticleGoogle Scholar
- Virgós E, Cabezas-Díaz S, Lozano J: Is the wild rabbit (Oryctolagus cuniculus) a threatened species in Spain? Sociological constraints in the conservation of species. Biodivers Conserv. 2007, 16: 3489-3504. 10.1007/s10531-006-9054-5.View ArticleGoogle Scholar
- Barrio IC, Bueno CG, Tortosa FS: Alternative food and rabbit damage in vineyards of southern Spain. Agric Ecosyst Environ. 2010, 138: 51-54. 10.1016/j.agee.2010.03.017.View ArticleGoogle Scholar
- Cooch EG, Conn PB, Ellner SP, Dobson AP, Pollock KH: Disease dynamics in wild populations: modeling and estimation: a review. J Ornithol. 2010, 152: 485-509.View ArticleGoogle Scholar
- Fordham DA, Sinclair RG, Peacock DE, Mutze GJ, Kovaliski J, Cassey P, Capucci L, Brook BW: European rabbit survival and recruitment are linked to epidemiological and environmental conditions in their exotic range. Austral Ecol. 2012, 37: 945-957. 10.1111/j.1442-9993.2011.02354.x.View ArticleGoogle Scholar
- Guitton J-S, Devillard S, Guénézan M, Fouchet D, Pontier D, Marchandeau S: Vaccination of free-living juvenile wild rabbits (Oryctolagus cuniculus) against myxomatosis improved their survival. Prev Vet Med. 2008, 84: 1-10. 10.1016/j.prevetmed.2007.10.001.View ArticlePubMedGoogle Scholar
- Arnason AN: The estimation of population size, migration rates and survival in a stratified population. Res Popul Ecol. 1973, 15: 1-8. 10.1007/BF02510705.View ArticleGoogle Scholar
- Senar JC, Conroy MJ: Multi-state analysis of the impacts of avian pox on a population of Serins (Serinus serinus): the importance of estimating recapture rates. Anim Biodivers Conserv. 2004, 27: 133-146.Google Scholar
- Faustino CR, Jennelle CS, Connolly V, Davis AK, Swarthout EC, Dhondt AA, Cooch EG: Mycoplasma gallisepticum infection dynamics in a house finch population: seasonal variation in survival, encounter and transmission rate. J Anim Ecol. 2004, 73: 651-669. 10.1111/j.0021-8790.2004.00840.x.View ArticleGoogle Scholar
- Conn PB, Cooch EG: Multistate capture-recapture analysis under imperfect state observation: an application to disease models. J Appl Ecol. 2009, 46: 486-492. 10.1111/j.1365-2664.2008.01597.x.View ArticleGoogle Scholar
- Pradel R: Multievent, an extension of multistate capture-recapture models to uncertain states. Biometrics. 2005, 61: 442-447. 10.1111/j.1541-0420.2005.00318.x.View ArticlePubMedGoogle Scholar
- Bruce JS, Twigg LE, Gray GS: The epidemiology of rabbit haemorrhagic disease, and its impact on rabbit populations, in south-western Australia. Wildl Res. 2004, 31: 31-49. 10.1071/WR02069.View ArticleGoogle Scholar
- Calvete C: The use of immunization programs in wild populations: Modelling effectiveness of vaccination campaigns against rabbit hemorrhagic disease. Biol Conserv. 2006, 130: 290-300. 10.1016/j.biocon.2005.12.025.View ArticleGoogle Scholar
- García-Bocanegra I, Astorga RJ, Napp S, Casal J, Huerta B, Borge C, Arenas A: Myxomatosis in wild rabbits: design of control programs in Mediterranean ecosystems. Prev Vet Med. 2010, 93: 42-50. 10.1016/j.prevetmed.2009.09.013.View ArticlePubMedGoogle Scholar
- Pinilla R, Tamajón R, Muñoz JM: Reconocimiento Biofísico de Espacios Naturales Protegidos. Edited by: Consejería de Medio Ambiente. 1995, Seville, Spain: Junta de Andalucía, 207-247. Vegetación actual, Parque Natural Sierra de Hornachuelos,Google Scholar
- Rouco C, Ferreras P, Castro F, Villafuerte R: The effect of exclusion of terrestrial predators on short-term survival of translocated European wild rabbits. Wildl Res. 2008, 35: 625-632. 10.1071/WR07151.View ArticleGoogle Scholar
- Bertó-Moran A, Pacios I, Serrano E, Moreno S, Rouco C: Coccidian and nematode infections influence prevalence of antibody to myxoma and rabbit hemorrhagic disease viruses in European rabbits. J Wildl Dis. 2013, 49: 10-17. 10.7589/2011-12-343.View ArticlePubMedGoogle Scholar
- Rouco C, Villafuerte R, Castro F, Ferreras P: Effect of artificial warren size on a restocked European wild rabbit population. Anim Conserv. 2011, 14: 117-123. 10.1111/j.1469-1795.2010.00401.x.View ArticleGoogle Scholar
- Soriguer RC: Biología y Dinámica de Una Población de Conejos (Oryctolagus cuniculus, L.) en Andalucía Occidental. 1981, Doñana: Acta Vertebrata 8Google Scholar
- Villafuerte R: Riesgo de predación y estrategias defensivas del conejo en el Parque Nacional de Doñana. PhD thesis. 1994, Córdoba: University of CórdobaGoogle Scholar
- Alves P, Moreno S: Estudo da reprodução do coelho-bravo (Oryctolagus cuniculus) em Portugal. Rev Florestal. 1996, 9: 149-166.Google Scholar
- OIE (World Organisation for Animal Health): Technical disease cards. 2013, [http://www.oie.int/en/animal-health-in-the-world/technical-disease-cards/]Google Scholar
- Choquet R, Reboulet A, Lebreton J, Gimenez O, Pradel R: U-CARE 2.2 User’s Manual. 2005, Montpellier, France: CEFEGoogle Scholar
- Choquet R, Lebreton J-D, Gimenez O, Reboulet A, Pradel R: U-CARE: utilities for performing goodness of fit tests and manipulating capture recapture data. Ecography. 2009, 32: 1-4.View ArticleGoogle Scholar
- Pradel R, Sanz-aguilar A: Modeling trap-awareness and related phenomena in capture-recapture studies. PLoS One. 2012, 7: 1-4.View ArticleGoogle Scholar
- Choquet R, Nogue E: E-SURGE 1.8 User’s Manual. (English edition). 2011, Montpellier, France: CEFEGoogle Scholar
- Pradel R: The Stakes of Capture-Recapture Models with State Uncertainty. Modeling Demographic Processes In Marked Populations. Edited by: Thomson DL, Cooch EG, Conroy MJ. 2009, New York: Springer, 781-795.View ArticleGoogle Scholar
- Genovart M, Pradel R, Oro D: Exploiting uncertain ecological fieldwork data with multi-event capture–recapture modelling: an example with bird sex assignment. J Anim Ecol. 2012, 81: 970-977. 10.1111/j.1365-2656.2012.01991.x.View ArticlePubMedGoogle Scholar
- Burnham KP, Anderson DR: Model Selection and Multimodel Inference: a Practical Information-theoretic Approach. 2002, New York: Springer, 2Google Scholar
- Lebreton JD, Burnham KP, Clobert J, Anderson DR: Modelling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecol Monogr. 1992, 62: 67-118. 10.2307/2937171.View ArticleGoogle Scholar
- Zipkin EF, Jennelle CS, Cooch EG: A primer on the application of Markov chains to the study of wildlife disease dynamics. Methods Ecol Evol. 2010, 1: 192-198. 10.1111/j.2041-210X.2010.00018.x.View ArticleGoogle Scholar
- Skalski JR, Hoffmann A, Smith SG: Testing the significance of individual-and cohort-level covariates in animal survival studies. Marked Individuals in the Study of Bird Population. Edited by: Lebreton JD, North PM. 1993, Bäsel, Switzerland: Birkhäuser, 9-28.Google Scholar
- Grosbois V, Tavecchia G: Modeling dispersal with capture - recapture data: disentangling decisions of leaving and settlement. Ecology. 2003, 84: 1225-1236. 10.1890/0012-9658(2003)084[1225:MDWCDD]2.0.CO;2.View ArticleGoogle Scholar
- Parkes JP, Norbury GL, Heyward RP, Sullivan G: Epidemiology of rabbit haemorrhagic disease (RHD) in the South Island, New Zealand, 1997–2001. Wildl Res. 2002, 29: 543-555. 10.1071/WR00108.View ArticleGoogle Scholar
- Fouchet D, Guitton J-S, Marchandeau S, Pontier D: Impact of myxomatosis in relation to local persistence in wild rabbit populations: the role of waning immunity and the reproductive period. J Theor Biol. 2008, 250: 593-605. 10.1016/j.jtbi.2007.10.037.View ArticlePubMedGoogle Scholar
- Fernández de Simón J, Díaz-Ruiz F, Cirilli F, Tortosa FS, Villafuerte R, Delibes-Mateos M, Ferreras P: Towards a standardized index of European rabbit abundance in Iberian Mediterranean habitats. Eur J Wildl Res. 2011, 57: 1091-1100. 10.1007/s10344-011-0524-z.View ArticleGoogle Scholar
- Moller H, Clapperton BK, Fletcher DJ: Density of rabbits (Oryctolagus cuniculus L.) in the Mackenzie Basin, South Island, New Zealand. N Z J Ecol. 1997, 21: 161-167.Google Scholar
- Mutze G, Kovaliski J, Butler K, Capucci L, McPhee S: The effect of rabbit population control programmes on the impact of rabbit haemorrhagic disease in south-eastern Australia. J Appl Ecol. 2010, 47: 1137-1146. 10.1111/j.1365-2664.2010.01844.x.View ArticleGoogle Scholar
- Boag B: Observations on the seasonal incidence of myxomatosis and its interactions with helminth parasites in the European rabbit (Oryctolagus cuniculus). J Wildl Dis. 1988, 24: 450-455. 10.7589/0090-3558-24.3.450.View ArticlePubMedGoogle Scholar
- Cattadori IM, Albert R, Boag B: Variation in host susceptibility and infectiousness generated by co-infection: the myxoma-Trichostrongylus retortaeformis case in wild rabbits. J R Soc Interface. 2007, 4: 831-840. 10.1098/rsif.2007.1075.PubMed CentralView ArticlePubMedGoogle Scholar
- Cattadori IM, Boag B, Hudson PJ: Parasite co-infection and interaction as drivers of host heterogeneity. Int J Parasitol. 2008, 38: 371-380. 10.1016/j.ijpara.2007.08.004.View ArticlePubMedGoogle Scholar
- Mitro S, Krauss H: Rabbit hemorrhagic disease: a review with special reference to its epizootiology. Eur J Epidemiol. 1993, 9: 70-78. 10.1007/BF00463093.View ArticlePubMedGoogle Scholar
- Ferreira PG, Costa-E-Silva A, Oliveira MJR, Monteiro E, Águas AP: Leukocyte-hepatocyte interaction in calicivirus infection: differences between rabbits that are resistant or susceptible to rabbit haemorrhagic disease (RHD). Vet Immunol Immunopathol. 2004, 103: 217-221.View ArticleGoogle Scholar
- Ferreira PG, Costa-e-Silva A, Monteiro E, Oliveira MJR, Águas AP: Liver enzymes and ultrastructure in rabbit haemorrhagic disease (RHD). Vet Res Commun. 2006, 30: 393-401. 10.1007/s11259-006-3297-1.View ArticlePubMedGoogle Scholar
- Calvete C, Estrada R, Villafuerte R, Osácar JJ, Lucientes J: Epidemiology of viral haemorrhagic disease and myxomatosis in a free-living population of wild rabbits. Vet Rec. 2002, 150: 776-782. 10.1136/vr.150.25.776.View ArticlePubMedGoogle Scholar
- Calvete C, Estrada R, Lucientes J, Osacar JJ, Villafuerte R: Effects of vaccination against viral haemorrhagic disease and myxomatosis on long-term mortality rates of European wild rabbits. Vet Rec. 2004, 155: 388-392. 10.1136/vr.155.13.388.View ArticlePubMedGoogle Scholar
- Kerr PJ, Hone J, Perrin L, French N, Williams CK: Molecular and serological analysis of the epidemiology of myxoma virus in rabbits. Vet Microbiol. 2010, 143: 167-178. 10.1016/j.vetmic.2009.11.025.View ArticlePubMedGoogle Scholar
- Foronda PR, Figueruelo EO, Ortega AR, Abreu NA, Casanova JC: Parasites (viruses, coccidian and helminthes) of the wild rabbit (Oryctolagus cuniculus) introduced to Canary Islands from Iberian Peninsula. Acta Parasitol. 2005, 50: 80-84.Google Scholar
- Cooke BD, Fenner F: Rabbit haemorrhagic disease and the biological control of wild rabbits, Oryctolagus cuniculus, in Australia and New Zealand. Wildl Res. 2002, 29: 689-706. 10.1071/WR02010.View ArticleGoogle Scholar
- Calvete C: Modeling the effect of population dynamics on the impact of rabbit hemorrhagic disease. Conserv Biol. 2005, 20: 1232-1241.View ArticleGoogle Scholar
- Cotilla I, Delibes-Mateos M, Ramírez E, Castro F, Cooke BD, Villafuerte R: Establishing a serological surveillance protocol for rabbit hemorrhagic disease by combining mathematical models and field data: implication for rabbit conservation. Eur J Wildl Res. 2010, 56: 725-733. 10.1007/s10344-010-0368-y.View ArticleGoogle Scholar
- Lachish S, Knowles SCL, Alves R, Wood MJ, Sheldon BC: Infection dynamics of endemic malaria in a wild bird population: parasite species-dependent drivers of spatial and temporal variation in transmission rates. J Anim Ecol. 2011, 80: 1207-1216. 10.1111/j.1365-2656.2011.01893.x.View ArticlePubMedGoogle Scholar
- Lachish S, Knowles SCL, Alves R, Wood MJ, Sheldon BC: Fitness effects of endemic malaria infections in a wild bird population: the importance of ecological structure. J Anim Ecol. 2011, 80: 1196-1206. 10.1111/j.1365-2656.2011.01836.x.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.