Wolbachia infections that reduce immature insect survival: Predicted impacts on population replacement
- Philip R Crain^{1},
- James W Mains^{1},
- Eunho Suh^{1},
- Yunxin Huang^{2},
- Philip H Crowley^{2} and
- Stephen L Dobson^{1}Email author
DOI: 10.1186/1471-2148-11-290
© Crain et al; licensee BioMed Central Ltd. 2011
Received: 26 May 2011
Accepted: 5 October 2011
Published: 5 October 2011
Abstract
Background
The evolutionary success of Wolbachia bacteria, infections of which are widespread in invertebrates, is largely attributed to an ability to manipulate host reproduction without imposing substantial fitness costs. Here, we describe a stage-structured model with deterministic immature lifestages and a stochastic adult female lifestage. Simulations were conducted to better understand Wolbachia invasions into uninfected host populations. The model includes conventional Wolbachia parameters (the level of cytoplasmic incompatibility, maternal inheritance, the relative fecundity of infected females, and the initial Wolbachia infection frequency) and a new parameter termed relative larval viability (RLV), which is the survival of infected larvae relative to uninfected larvae.
Results
The results predict the RLV parameter to be the most important determinant for Wolbachia invasion and establishment. Specifically, the fitness of infected immature hosts must be close to equal to that of uninfected hosts before population replacement can occur. Furthermore, minute decreases in RLV inhibit the invasion of Wolbachia despite high levels of cytoplasmic incompatibility, maternal inheritance, and low adult fitness costs.
Conclusions
The model described here takes a novel approach to understanding the spread of Wolbachia through a population with explicit dynamics. By combining a stochastic female adult lifestage and deterministic immature/adult male lifestages, the model predicts that even those Wolbachia infections that cause minor decreases in immature survival are unlikely to invade and spread within the host population. The results are discussed in relation to recent theoretical and empirical studies of natural population replacement events and proposed applied research, which would use Wolbachia as a tool to manipulate insect populations.
Background
Prior models highlight three Wolbachia-specific parameters that affect the probability of Wolbachia invasion and establishment: the maternal inheritance rate, which is the proportion of infected offspring produced by an infected female; the level of CI, which is the proportion of embryos that fail to develop as a result of incompatible crosses [14]; and the fitness cost to females for carrying a Wolbachia infection, defined as a decrease in overall fecundity [15–20].
Previous studies predict that the successful invasion of Wolbachia into an uninfected host population requires low fecundity costs, high maternal inheritance rates, and high levels of CI [21, 22]. Wolbachia infections that impose a 10% relative fecundity cost to adult females experience reductions in their invasion success [21]. Similarly, low maternal inheritance reduces the probability of Wolbachia invasion [22]. Higher initial Wolbachia infection frequencies are predicted to increase the probability of population replacement, which can offset the above costs [14]. Models have also addressed population structure at the adult stage, impacts on adult survival, stochastic effects, and overlapping generations [14, 21–25].
The relative importance of Wolbachia effects on immature life stages has not been assessed theoretically. This is despite multiple examples demonstrating an effect of Wolbachia on immature hosts. In the stored product pest Liposcelis tricolor (Psocoptera: Liposcelidae), Wolbachia infections can decrease development periods and increase survivorship in some immature life stages [26]. Other studies demonstrate negative impacts of Wolbachia infections on larval survival and development time [27, 28]. Recent studies have determined that when intraspecific competition is intense, Wolbachia-infected mosquito larvae experience reduced survival [29, 30].
To better understand population replacement by CI-inducing Wolbachia, we have evaluated both Wolbachia infection dynamics and host population dynamics using a model that includes deterministic immature and adult male lifestages and a stochastic adult female lifestage. Since Wolbachia are transmitted maternally, the sex and infection status of hosts are explicit, and adult females are tracked individually. The focus of this modeling approach was to investigate changes in the probability of population replacement resulting from varying the relative larval viability (RLV), expressed as relative survival of infected to uninfected larvae. The results are presented in context with traditional parameters: the rate of CI, maternal inheritance (MI), the relative fecundity of infected females (RF), and the initial Wolbachia infection frequency (IF), on the probability of population replacement.
Methods
The model simulates a panmictic population that is closed to immigrants and emigrants. Consistent with previous studies, the model assumes mating is random and that Wolbachia infection has no effect on mating success. Females in the model mate once immediately upon reaching maturity. Adult survival is density-independent, but larval survival is density-dependent. The model presented here combines a stochastic adult female stage with deterministic adult male and immature stages. By implementing a deterministic immature stage, additional information regarding population dynamics is incorporated without developing a completely stochastic model, which would be considerably more computationally-intensive. The model incorporates overlapping generations [24] while tracking major life stages and considers females and males separately. Development time and survival during immature stages are addressed explicitly by the model. The model was designed assuming the host is a holometabolous insect, and the model was parameterized based upon estimates of mosquitoes in the genus Aedes as a case study.
Brief Description of Equations
The following is a brief overview of all equations and parameters implemented in the model presented here. Additional development details, initial parameter values, and sensitivity analysis are provided in Additional File 1.
Egg production, E: u is the egg production rate; Δt is the time step (units of time); v is the female mass coefficient (units of (mass)^{-1}); M_{ f }is the body mass of the ovipositing female (units of mass); w is the female mass intercept (units of mass), and z is the female mass exponent (dimensionless). Derived by combining two previously published functions [38, 39].
Immature Life Stages
Larvae develop through discrete developmental stages, where the development rate is affected by density dependence, and larval survival is subject to both stage-dependent mortality and density-dependence (Figure 2b). The term "stage" is defined here as a measure of progress through larval development. The number of these discrete developmental stages is chosen to allow for variation in development time and is otherwise arbitrary (i.e., not linked to age or developmental instar explicitly). The number of larval developmental stages, s, can be varied, but was set to s = 30 for this study. Larval development rate, R, is the number of developmental stages through which a cohort of larvae will pass within 24 hours (Equation 1). The number of larvae surviving to the next day is the product of the number of larvae in the preceding time period and the larval survival rate (Equation 2). When the number of developmental stages within a day is not an integer, the larval cohort is distributed into two adjacent developmental stages in proportions that preserve the average development rate. The latter also introduces variation into the development rates of larval cohorts (Figure 2b). Density-dependence is based on the total mass of larvae (Equation 3). Male and female cohorts are considered separately to observe sex-specific patterns during development. For example, female mosquitoes require longer development time to become adults relative to males, and studies demonstrate that males and females respond to competition intensities differently [30].
Glossary of notation, including the initial values for each key parameter
symbol | definition | initial value |
---|---|---|
CI | proportion of embryos not hatching in incompatible CI crosses | 0.999 |
MI | proportion of offspring receiving infection (maternal inheritance) | 0.999 |
RF | relative fecundity of infected females to uninfected females | 0.999 |
RLV | relative larval viability of infected larvae to uninfected larvae | 0.999 |
IF | initial frequency of gravid infected females to the total adult population | 0.500 |
Following the completion of larval development stages, individuals become non-feeding pupae, which have a daily survival that is independent of population density (S_{ p }, Table S1; Figure 2c). After completing pupal development, emerging male adults are tracked separately as either infected or uninfected cohorts. Emerging female adults are tracked as individuals.
Adult Life Stages
Six variables are tracked over time and determine the state of individual females: the blood meal state (time since last feeding), age (days since emerging), Wolbachia infection status (infected or uninfected), the Wolbachia infection status of her mate (determined randomly based on the proportion of infected males in the population at the time she mates), size (body mass), and reproductive state (the number of gonotrophic cycles completed).
The probability that a female obtains a blood meal is determined by the frequency of potential blood meals per unit area, and each blood meal is associated with an additional mortality risk, regardless of mosquito age (Table S1). In the panmictic population simulated here, the availability of potential blood meals is assumed to be constant, but the model will allow downstream population structuring and geographic variation of bloodmeal availability.
Adult female daily survivorship F_{ s }is age-dependent and probabilistic (Equation 4) [37]. A female that is Wolbachia uninfected and mated with an infected male will lay eggs, but a proportion of the eggs will not hatch, depending on the level of CI (Table 1). Infected females produce viable offspring regardless of their mate's infection status but are subject to a decrease in relative fecundity (RF, Table 1). The number of eggs laid by an individual female is determined by her mass (Equation 5), and larval development influences female body mass. Specifically, intense competition delays development and reduces the mass of adult females.
Adult males, which are dead end hosts for Wolbachia, are not tracked individually but are tracked as infected and uninfected cohorts. The male mortality rate is assumed to be age-independent and constant (S_{ M }, Table S1). The proportion of Wolbachia infected males in the population determines the probability of an incompatible mating for uninfected females.
Simulations
Results
Figure 3 provides an example of the typical population dynamics resulting from model simulations of a Wolbachia population replacement event. In the illustrated example, the population begins as cohort of uninfected eggs and stabilizes after approximately 150 days, with variation around a consistent population size and lifestage distribution (Figure 3a). In the example simulation, the introduction of Wolbachia occurs at day 800 by introducing blood-fed, gravid adult females at an initial Wolbachia infection frequency (IF) of 0.5 (Table 1). IF is the frequency of Wolbachia-infected females relative to the total number of adults such that an IF = 1 is synonymous with a 1:1 (infected to uninfected) ratio. Figure 3b illustrates the resulting variation in Wolbachia infection frequency in the host population versus time.
Due to the stochastic nature of the model, the number of individuals within each lifestage fluctuates considerably over time (Figure 3a). To examine for temporal patterns in the fluctuations that might correspond to periodic signals such as stage durations or generation time, we performed a spectral analysis on the time series data for both total adult and larval populations via Fast Fourier Transformation [42, 43]. The analysis can identify temporal patterns that exist in what appear to be chaotic time series. No pattern was detected by the spectral analysis. Since no period was found, stochasticity appears to be the sole driver of population fluctuations.
Five parameters associated with Wolbachia infection were evaluated for their affect on the probability of population replacement. The value of each parameter was varied at one one-hundredth increments, from zero to one, while additional parameters were held constant as defined in Table 1. For each parameter value, the probability of population replacement was determined by the number of successful replacement events occurring in 1000 simulations, for a total of 101,000 simulations per parameter.
A different functional relationship is observed with the level of incompatibility (CI) and initial Wolbachia infection frequency (IF), each of which results in response curves that increase asymptotically (Figure 4). Assuming the parameters within Table 1, the model predicts that CI is not necessary for Wolbachia to spread (i.e., approximately 7% of simulations resulted in population replacement when CI = 0). Realistic probabilities of population replacement occur when CI approaches 0.3. Despite perfect CI (i.e., no egg hatch in incompatible crosses), population replacement did not occur in 10% of simulations (Figure 4). Additional simulations confirmed that a 90% probability of population replacement is an absolute maximum given the conditions defined here (Table 1). However, as the magnitude of IF increases, the probability of population replacement rapidly approaches one, with realistic probabilities of population replacement occurring when the frequency of infected females approaches 20% (Figure 4).
The probability of population replacement for given parameter values
MI | RF | ||
---|---|---|---|
1.0 | 0.9 | 0.8 | |
1.0 | 0.1023/0.0359 | 0.0224/0.0089 | 0.0004/0.0007 |
0.9 | 0.0158/0.0060 | 0.0004/0.0000 | / |
0.8 | 0.0001/0.0004 | / | / |
Jansen et al. [22]/model presented here |
Discussion
The model presented here examines the probabilities of Wolbachia invasion into an isolated uninfected population. The model is unique in its individual-based representation of variation in key traits among adult females and in the resolution of larval dynamics within the host population. The model presented here predicts, as in previous modeling studies, that maternal inheritance (MI) and the relative fecundity of adult females (RF) are key parameters that determine the potential for population replacement. Specifically, population replacement occurs only at high MI or RF. In contrast, population replacement can occur at low CI or low IF. The simulation of adult females as individuals demonstrates that MI requires higher parameter values than RF for successful population replacement. The new parameter, relative larval viability (RLV), like MI and RF, requires high parameter values before population replacement can occur.
The relative larval viability between Wolbachia infected and uninfected individuals (RLV) is the most important determinant of population replacement, requiring the highest parameter values for invasion. The model predicts that reductions in infected larval survival can substantially reduce the probability of population replacement (Figure 4). While a majority of prior studies have examined for an effect in adults, recent studies have determined that, at high levels of intraspecific competition, Wolbachia infected larvae experience reduced survival [29]. However, few theoretical studies have examined the impact of immature lifestages on the invasion of Wolbachia. Here, we demonstrate that reductions in RLV will inhibit Wolbachia invasion into an uninfected host population.
Recent work has highlighted the prevalence of Wolbachia, and its ability to invade populations [1, 20]. Studies have suggested that Wolbachia infection affects larval survival and development only when intraspecific competition is high [29, 44]. Given the predictions from our model, Wolbachia can only invade a population when RLV is very high. Therefore, the density of conspecifics in larval habitats is predicted to have significant impacts on the probability of population replacement. Similarly, the abundance and variety of larval habitats may have significant impact on the invasion of Wolbachia. The distribution, utilization and variety of larval habitats is well known for some insects, particularly mosquitoes [45–48]. Theoretical studies considering the effect of metapopulation structure and larval rearing conditions may elucidate the mechanism by which Wolbachia can invade natural populations given low initial infection frequencies.
The level of CI in insects varies widely [44, 49–51]. Our model shows that the intensity of CI has relatively little effect on the probability of population replacement when the rate of CI exceeds 60%. Furthermore, when CI = 0, the model presented here predicts population replacement can occur at low probabilities (Figure 4). Some Wolbachia infections do not cause CI, but are found at high frequencies in natural populations [44, 50, 52]. Previous theoretical studies indicate that CI or a sex-ratio distorter is not required for population replacement when endosymbionts can alter female traits [44, 53]. However, results presented here suggest that non-CI inducing Wolbachia infections can establish and persist in a population without increasing or altering host fitness, given high MI, RF, and RLV. Since the population considered by the model presented here is relatively small (N ≈ 110 adults), genetic drift could perhaps influence the probability of population replacement [54]. To investigate the importance of genetic drift, the population size in the model was increased. In model simulations where the total adult population size is greater than approximately 200, population replacement does not occur when there is no effect of CI (i.e. CI = 0). However, when population size is increased, the general response patterns in Figure 4 are not altered.
High maternal inheritance rates have been observed consistently in natural populations [55–57]. Furthermore, theoretical studies predict the probability of population replacement declines as maternal inheritance decreases [12, 21, 22]. Similar to previous studies, results presented here suggest that maternal inheritance (MI) must be high for a Wolbachia infection to invade an uninfected population and persist. Specifically, MI must be higher than 90% to attain a realistic probability of population replacement.
The effect of Wolbachia infections on adult female fitness has been well documented empirically and theoretically [11, 15, 16, 22, 24, 58, 59]. Here, as in previous theoretical studies, the model predicts that the relative fecundity of adult females (RF) must be high to facilitate population replacement.
For all parameters, the probability of population replacement approached an absolute maximum of 90% given the conditions defined in Table 1. Here, the initially examined IF value is relatively high (0.5), analogous to artificial introductions examined in prior theoretical work [25]. Subsequently, lower IF values have been simulated (Figure 4), including the introduction of a single, infected female (Table 2). The model predicts that Wolbachia invasion can occur at the lowest IF values and demonstrates an increasing probability of invasion with the higher introduction levels, with the probability of population replacement approaching 100%. Additional simulations determined that when IF is held constant and the total adult population size is increased, the probability of population replacement approaches one given the conditions defined in Table 1. This result suggests genetic drift can affect the probability of population replacement in small populations and may facilitate or hinder the spread of Wolbachia from low initial frequencies [54].
The model presented here predicted lower population replacement probabilities than those predicted by previous stochastic models (Table 2 and Figure 5) [22]. Rasgon and Scott [25] noted a similar behavior where implementing population age-structure and overlapping generations increased deterministic thresholds. The inclusion of additional life stages and stage-structure in this stochastic model may explain the reduced probabilities of population replacement. However, the model presented here predicted marginally higher probabilities of population replacement when either maternal inheritance or the relative fecundity of infected females had a magnitude of 0.8. The increased probability of population replacement predicted by the model presented here is likely a result of the individual-based representation of the adult female life stage that includes stochastic survival.
The model here addresses a single, panmictic, isolated population but could be expanded to include metapopulation structure. If introduction events can be assumed to occur randomly, then the surrounding subpopulations should generally tend to inhibit population replacement, because migration between subpopulations would dilute the proportion of infected individuals. However, as demonstrated here, genetic drift may influence the invasion of Wolbachia in smaller subpopulations. The spatial spread of Wolbachia has been assessed analytically by others and defines the conditions needed for Wolbachia to spread through space [20, 24].
The majority of models that address the invasion of Wolbachia into uninfected populations have examined populations without lifestage subdivisions, suggesting that additional empirical studies focused on understanding larval dynamics are needed [34]. Many of the parameters defined here may be difficult to determine in natural populations [25], but our results demonstrate the importance of understanding the role of life history parameters and their interactions, despite the difficulties. Furthermore, the sensitivity analysis of the model presented here demonstrates that the magnitudes of particular parameters strongly influence the potential for spread and establishment of Wolbachia; these (e.g., Wolbachia effects on immature fitness) should be the focus of future empirical and theoretical studies. Future theoretical studies could further address parameter sensitivity by hyper-cube sampling, but this would require information about the distribution of parameters to investigated [60].
Conclusions
Wolbachia is currently being utilized as the basis for a gene drive strategy in open field releases of Aedes aegypti [61, 62]; however, the predictions of the model presented here suggest that minute reductions in RLV can inhibit population replacement. Research needs to focus on understanding the effects of novel Wolbachia infections on immature lifestages. Xi et al. [63] demonstrated that novel Wolbachia infections can establish in a new host species and replace an uninfected population, but the initial frequency of Wolbachia infected individuals needed to replace the population was higher than predicted. The authors suggested that differences in survival of immature lifestages could explain their results. Results presented here indicate that even reductions in RLV that are difficult to detect empirically will substantially reduce the probability of population replacement.
The rapid decline in the probability of population replacement associated with reduced larval viability indicates that empirical studies directed toward quantifying the effects of endosymbionts on immature insects are important for understanding and predicting Wolbachia invasion events. Recent empirical studies also suggest that a more complete understanding of the effects of Wolbachia on the immature life stages is generally needed through additional empirical and theoretical studies [28–30].
Declarations
Acknowledgements
The authors would like to thank Michael Turelli and Peter Hammerstein for comments and suggestions on this project. This research was supported by grants from the National Institutes of Health [AI-067434] and the Bill and Melinda Gates Foundation [#44190]. This is publication 11-08-042 of the University of Kentucky Agricultural Experiment Station.
Authors’ Affiliations
References
- Hilgenboecker K, Hammerstein P, Schlattmann P, Telschow A, Werren JH: How many species are infected with Wolbachia? - A statistical analysis of current data. Fems Microbiology Letters. 2008, 281 (2): 215-220. 10.1111/j.1574-6968.2008.01110.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Werren JH: Biology of Wolbachia. Annu Rev Entomol. 1997, 42: 587-609. 10.1146/annurev.ento.42.1.587.View ArticlePubMedGoogle Scholar
- Werren JH, Baldo L, Clark ME: Wolbachia: Master manipulators of invertebrate biology. Nature Reviews Microbiology. 2008, 6 (10): 741-751. 10.1038/nrmicro1969.View ArticlePubMedGoogle Scholar
- Hurst GDD, Jiggins FM, von der Schulenburg JHG, Bertrand D, West SA, Goriacheva II, Zakharov IA, Werren JH, Stouthamer R, Majerus MEN: Male-killing Wolbachia in two species of insect. P Roy Soc Lond B Bio. 1999, 266 (1420): 735-740. 10.1098/rspb.1999.0698.View ArticleGoogle Scholar
- Hornett EA, Charlat S, Wedell N, Jiggins CD, Hurst GDD: Rapidly shifting sex ratio across a species range. Curr Biol. 2009, 19 (19): 1628-1631. 10.1016/j.cub.2009.07.071.View ArticlePubMedGoogle Scholar
- Bouchon D, Rigaud T, Juchault P: Evidence for widespread Wolbachia infection in isopod crustaceans: Molecular identification and host feminization. P Roy Soc Lond B Bio. 1998, 265 (1401): 1081-1090. 10.1098/rspb.1998.0402.View ArticleGoogle Scholar
- Kobayashi Y, Telschow A: Cytoplasmic feminizing elements in a two-population model: Infection dynamics, gene flow modification, and the spread of autosomal suppressors. J Evol Biol. 2010, 23 (12): 2558-2568. 10.1111/j.1420-9101.2010.02116.x.View ArticlePubMedGoogle Scholar
- Huigens ME, Luck RF, Klaassen RHG, Maas MFPM, Timmermans MJTN, Stouthamer R: Infectious parthenogenesis. Nature. 2000, 405 (6783): 178-179. 10.1038/35012066.View ArticlePubMedGoogle Scholar
- Kremer N, Charif D, Henri H, Bataille M, Prevost G, Kraaijeveld K, Vavre F: A new case of Wolbachia dependence in the genus Asobara: Evidence for parthenogenesis induction in Asobara japonica. Heredity. 2009, 103 (3): 248-256. 10.1038/hdy.2009.63.View ArticlePubMedGoogle Scholar
- Stouthamer R, Russell JE, Vavre F, Nunney L: Intragenomic conflict in populations infected by parthenogenesis inducing Wolbachia ends with irreversible loss of sexual reproduction. BMC Evol Biol. 2010, 10:Google Scholar
- Turelli M, Hoffmann AA: Cytoplasmic incompatibility in Drosophila simulans dynamics and parameter estimates from natural populations. Genetics. 1995, 140 (4): 1319-1338.PubMedPubMed CentralGoogle Scholar
- Farkas JZ, Hinow P: Structured and unstructured continuous models for Wolbachia infections. Bull Math Biol. 2010, 72 (8): 2067-2088. 10.1007/s11538-010-9528-1.View ArticlePubMedGoogle Scholar
- Dobson SL, Fox CW, Jiggins FM: The effect of Wolbachia-induced cytoplasmic incompatibility on host population size in natural and manipulated systems. P Roy Soc Lond B Bio. 2002, 269 (1490): 437-445. 10.1098/rspb.2001.1876.View ArticleGoogle Scholar
- Engelstadter J, Telschow A: Cytoplasmic incompatibility and host population structure. Heredity. 2009, 103 (3): 196-207. 10.1038/hdy.2009.53.View ArticlePubMedGoogle Scholar
- Caspari E, Watson GS: On the evolutionary importance of cytoplasmic sterility in mosquitos. Evolution. 1959, 13 (4): 568-570. 10.2307/2406138.View ArticleGoogle Scholar
- Fine PEM: Dynamics of symbiote-dependent cytoplasmic incompatibility in Culicine mosquitos. J Invertebr Pathol. 1978, 31 (1): 10-18. 10.1016/0022-2011(78)90102-7.View ArticlePubMedGoogle Scholar
- Hoffmann AA, Turelli M, Harshman LG: Factors affecting the distribution of cytoplasmic incompatibility in Drosophila simulans. Genetics. 1990, 126 (4): 933-948.PubMedPubMed CentralGoogle Scholar
- Hurst LD: The evolution of cytoplasmic incompatibility or when spite can be successful. J Theor Biol. 1991, 148 (2): 269-277. 10.1016/S0022-5193(05)80344-3.View ArticlePubMedGoogle Scholar
- Turelli M: Evolution of incompatibility-inducing microbes and their hosts. Evolution. 1994, 48 (5): 1500-1513. 10.2307/2410244.View ArticleGoogle Scholar
- Turelli M, Hoffmann AA: Rapid spread of an inherited incompatibility factor in California Drosophila. Nature. 1991, 353 (6343): 440-442. 10.1038/353440a0.View ArticlePubMedGoogle Scholar
- Egas M, Vala F, Breeuwer JAJ: On the evolution of cytoplasmic incompatibility in haplodiploid species. Evolution. 2002, 56 (6): 1101-1109.View ArticlePubMedGoogle Scholar
- Jansen VAA, Turelli M, Godfray HCJ: Stochastic spread of Wolbachia. Proceedings of the Royal Society B-Biological Sciences. 2008, 275 (1652): 2769-2776. 10.1098/rspb.2008.0914.View ArticlePubMed CentralGoogle Scholar
- Haygood R, Turelli M: Evolution of incompatibility inducing microbes in subdivided host populations. Evolution. 2009, 63 (2): 432-447. 10.1111/j.1558-5646.2008.00550.x.View ArticlePubMedGoogle Scholar
- Turelli M: Cytoplasmic incompatibility in populations with overlapping generations. Evolution. 2010, 64 (1): 232-241. 10.1111/j.1558-5646.2009.00822.x.View ArticlePubMedGoogle Scholar
- Rasgon JL, Scott TW: Impact of population age structure on Wolbachia transgene driver efficacy: Ecologically complex factors and release of genetically modified mosquitoes. Insect Biochem Molec. 2004, 34 (7): 707-713. 10.1016/j.ibmb.2004.03.023.View ArticleGoogle Scholar
- Dong P, Wang JJ, Hu F, Jia FX: Influence of Wolbachia infection on the fitness of the stored-product pest Liposcelis tricolor (Psocoptera: Liposeelididae). J Econ Entomol. 2007, 100 (4): 1476-1481. 10.1603/0022-0493(2007)100[1476:IOWIOT]2.0.CO;2.View ArticlePubMedGoogle Scholar
- Islam MS, Dobson SL: Wolbachia effects on Aedes albopictus (Diptera: Culicidae) immature survivorship and development. J Med Entomol. 2006, 43 (4): 689-695. 10.1603/0022-2585(2006)43[689:WEOAAD]2.0.CO;2.View ArticlePubMedGoogle Scholar
- McMeniman CJ, O'Neill SL: A virulent Wolbachia infection decreases the viability of the Dengue vector Aedes aegypti during periods of embryonic quiescence. Plos Neglect Trop D. 2010, 4 (7):
- Gavotte L, Mercer DR, Stoeckle JJ, Dobson SL: Costs and benefits of Wolbachia infection in immature Aedes albopictus depend upon sex and competition level. J Invertebr Pathol. 2010, 105 (3): 341-346. 10.1016/j.jip.2010.08.005.View ArticlePubMedPubMed CentralGoogle Scholar
- Gavotte L, Mercer DR, Vandyke R, Mains JW, Dobson SL: Wolbachia infection and resource competition effects on immature Aedes albopictus (Diptera: Culicidae). J Med Entomol. 2009, 46 (3): 451-459. 10.1603/033.046.0306.View ArticlePubMedPubMed CentralGoogle Scholar
- Barbosa PP, Greenough MT, N C: Overcrowding of mosquito populations: Responses of larva Aedes aegypti to stress. Environmental Entomology. 1972, 1 (1): 89-93.View ArticleGoogle Scholar
- Peters TM, Barbosa P: Influence of population-density on size, fecundity, and developmental rate of insects in culture. Annu Rev Entomol. 1977, 22: 431-450. 10.1146/annurev.en.22.010177.002243.View ArticleGoogle Scholar
- Dye C: Model for the population-dynamics of the Yellow Fever mosquito, Aedes aegypti. J Anim Ecol. 1984, 53 (1): 247-268. 10.2307/4355.View ArticleGoogle Scholar
- Magori K, Legros M, Puente ME, Focks DA, Scott TW, Lloyd AL, Gould F: Skeeter Buster: A stochastic, spatially explicit modeling tool for studying Aedes aegypti population replacement and population suppression strategies. Plos Neglect Trop D. 2009, 3 (9):
- Focks DA, Haile DG, Daniels E, Mount GA: Dynamic life table model for Aedes aegypti (Diptera: Culicidae) - Simulation and validation. J Med Entomol. 1993, 30 (6): 1018-1028.View ArticlePubMedGoogle Scholar
- Southwood T, Murdie G, Yasuno M, Tonn R, Reader P: Studies on the life budget of Aedes aegypti in Wat Samphaya, Bangkok, Thailand. Bulletin of the World Health Organization. 1972, 46: 211-226.PubMedPubMed CentralGoogle Scholar
- Trpis M, Hausermann W: Dispersal and other population parameters of Aedes aegypti in an African village and their possible significance in epidemiology of vector-borne diseases. Am J Trop Med Hyg. 1986, 35 (6): 1263-1279.PubMedGoogle Scholar
- Blackmore MS, Lord CC: The relationship between size and fecundity in Aedes albopictus. Journal of Vector Ecology. 2000, 25 (2): 212-217.PubMedGoogle Scholar
- Lounibos LP, Rey JR, Frank JH: Ecology of mosquitoes: Proceedings of a workshop. 1985, Vero Beach, Fla.: Florida Medical Entomology LaboratoryGoogle Scholar
- Gillett JD, Roman EA, Phillips V: Erratic hatching in Aedes eggs - New interpretation. P Roy Soc Lond B Bio. 1977, 196 (1123): 223-232. 10.1098/rspb.1977.0038.View ArticleGoogle Scholar
- Christophers SR: Aëdes aegypti (L.), the Yellow fever mosquito; its life history, bionomics, and structure. 1960, Cambridge Eng.: University PressGoogle Scholar
- Wijnen H, Naef F, Young MW: Molecular and statistical tools for circadian transcript profiling. Methods Enzymol. 2005, 393: 341-365.View ArticlePubMedGoogle Scholar
- Keegan KP, Pradhan S, Wang JP, Allada R: Meta-analysis of Drosophila circadian microarray studies identifies a novel set of rhythmically expressed genes. PLoS Comput Biol. 2007, 3 (11): 2087-2110.View ArticleGoogle Scholar
- Hoffmann AA, Clancy D, Duncan J: Naturally-occurring Wolbachia infection in Drosophila simulans that does not cause cytoplasmic incompatibility. Heredity. 1996, 76: 1-8. 10.1038/hdy.1996.1.View ArticlePubMedGoogle Scholar
- Aldstadt J, Koenraadt CJM, Fansiri T, Kijchalao U, Richardson J, Jones JW, Scott TW: Ecological modeling of Aedes aegypti (L.) pupal production in rural Kamphaeng Phet, Thailand. Plos Neglect Trop D. 2011, 5 (1):
- Harrington LC, Ponlawat A, Edman JD, Scott TW, Vermeylen F: Influence of container size, location, and time of day on oviposition patterns of the Dengue vector, Aedes aegypti, in Thailand. Vector-Borne Zoonotic Dis. 2008, 8 (3): 415-423. 10.1089/vbz.2007.0203.View ArticlePubMedPubMed CentralGoogle Scholar
- Harrington LC, Ponlawat A, Scott TW, Edman JD: Does container size influence oviposition choices of the dengue vector Aedes aegypti?. Am J Trop Med Hyg. 2005, 73 (6): 914-Google Scholar
- Koenraadt CJM, Aldstadt J, Kijchalao U, Sithiprasasna R, Getis A, Jones JW, Scott TW: Spatial and temporal patterns in pupal and adult production of the Dengue vector Aedes aegypti in Kamphaeng Phet, Thailand. Am J Trop Med Hyg. 2008, 79 (2): 230-238.PubMedGoogle Scholar
- Mercot H, Charlat S: Wolbachia infections in Drosophila melanogaster and D. simulans: Polymorphism and levels of cytoplasmic incompatibility. Genetica. 2004, 120 (1-3): 51-59.View ArticlePubMedGoogle Scholar
- Charlat S, Le Chat L, Mercot H: Characterization of non-cytoplasmic incompatibility inducing Wolbachia in two continental African populations of Drosophila simulans. Heredity. 2003, 90 (1): 49-55. 10.1038/sj.hdy.6800177.View ArticlePubMedGoogle Scholar
- Zabalou S, Apostolaki A, Pattas S, Veneti Z, Paraskevopoulos C, Livadaras I, Markakis G, Brissac T, Mercot H, Bourtzis K: Multiple rescue factors within a Wolbachia strain. Genetics. 2008, 178 (4): 2145-2160. 10.1534/genetics.107.086488.View ArticlePubMedPubMed CentralGoogle Scholar
- Hoffmann AA, Hercus M, Dagher H: Population dynamics of the Wolbachia infection causing cytoplasmic incompatibility in Drosophila melanogaster. Genetics. 1998, 148 (1): 221-231.PubMedPubMed CentralGoogle Scholar
- Hayashi TI, Marshall JL, Gavrilets S: The dynamics of sexual conflict over mating rate with endosymbiont infection that affects reproductive phenotypes. J Evol Biol. 2007, 20 (6): 2154-2164. 10.1111/j.1420-9101.2007.01429.x.View ArticlePubMedGoogle Scholar
- Hedrick PW: Genetics of populations. 2011, Sudbury, Mass.: Jones and Bartlett Publishers, 4Google Scholar
- Narita S, Nomura M, Kageyama D: Naturally occurring single and double infection with Wolbachia strains in the butterfly Eurema hecabe: transmission efficiencies and population density dynamics of each Wolbachia strain. Fems Microbiology Ecology. 2007, 61 (2): 235-245. 10.1111/j.1574-6941.2007.00333.x.View ArticlePubMedGoogle Scholar
- Poinsot D, Montchamp-Moreau C, Mercot H: Wolbachia segregation rate in Drosophila simulans naturally bi-infected cytoplasmic lineages. Heredity. 2000, 85 (2): 191-198. 10.1046/j.1365-2540.2000.00736.x.View ArticlePubMedGoogle Scholar
- Rasgon JL, Scott TW: Wolbachia and cytoplasmic incompatibility in the California Culex pipiens mosquito species complex: Parameter estimates and infection dynamics in natural populations. Genetics. 2003, 165 (4): 2029-2038.PubMedPubMed CentralGoogle Scholar
- Weeks AR, Reynolds KT, Hoffmann AA, Mann H: Wolbachia dynamics and host effects: What has (and has not) been demonstrated?. Trends Ecol Evol. 2002, 17 (6): 257-262. 10.1016/S0169-5347(02)02480-1.View ArticleGoogle Scholar
- Weeks AR, Turelli M, Harcombe WR, Reynolds KT, Hoffmann AA: From parasite to mutualist: Rapid evolution of Wolbachia in natural populations of Drosophila. PLoS Biol. 2007, 5 (5): 997-1005.View ArticleGoogle Scholar
- Kiparissides A, Kucherenko SS, Mantalaris A, Pistikopoulos EN: Global sensitivity analysis challenges in biological systems modeling. Ind Eng Chem Res. 2009, 48 (15): 7168-7180. 10.1021/ie900139x.View ArticleGoogle Scholar
- Marshall JM: The effect of gene drive on containment of transgenic mosquitoes. J Theor Biol. 2009, 258 (2): 250-265. 10.1016/j.jtbi.2009.01.031.View ArticlePubMedGoogle Scholar
- Enserink M: Australia to test 'mosquito vaccine' against human disease. Science. 2010, 330 (6010): 1460-1461. 10.1126/science.330.6010.1460.View ArticlePubMedGoogle Scholar
- Xi ZY, Khoo CCH, Dobson SL: Wolbachia establishment and invasion in an Aedes aegypti laboratory population. Science. 2005, 310 (5746): 326-328. 10.1126/science.1117607.View ArticlePubMedGoogle Scholar
Copyright
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 cited.