1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Avian haemosporidian infections (of the genera Haemoproteus, Plasmodium and Leucocytozoon) can regulate passerine populations. Thus, reduction in the number of avian haemosporidian infections in a population, for example in recently introduced hosts, may facilitate host establishment or spread (i.e. enemy release). Alternatively, colonizers could decrease competitive ability of native individuals in the novel range by increasing the prevalence of avian haemosporidians in that native passerine community (i.e. novel weapons). However, whether either or both of these phenomena will occur is difficult to predict because infection risk can be highly heterogeneous and dependent upon the interaction of biotic and abiotic factors at the microclimate level, especially because of the important role of vectors for these parasites. Here, we describe which factors best predicted avian haemosporidian prevalence in populations of house sparrows Passer domesticus introduced to Kenya. House sparrows inhabit an invasion gradient in Kenya; they were introduced via the eastern port city of Mombasa in ˜ 1950 and have since spread west-ward across the country. This range expansion gave us the opportunity to examine how parasite prevalence changes over small spatiotemporal scales and what role is played by environmental and individual traits. Among all individuals, body mass was the strongest predictor of infection, with larger house sparrows being more likely to be infected. At the population level, capture month, precipitation (higher prevalence with more rainfall), and population age (increasing prevalence with increasing time since introduction) were important risk factors. Overall, haemosporidian prevalence in Kenyan house sparrows appears to be more strongly associated with individual characteristics rather than with time since introduction as was predicted, though this does not necessarily rule out a role for enemy release or novel weapons in this system.

Avian haemosporidians of the genera Plasmodium, Haemoproteus and Leucocytozoon, often referred to as avian malaria, are obligate, intracellular parasites of red blood cells, vectored by mosquitoes, blackflies and biting midges (Valkiūnas 2005). These parasites have been valuable in understanding spatial and temporal host–parasite–vector dynamics (Bensch et al. 2007, Sehgal et al. 2011, Medeiros et al. 2013), co-infection (de Roode et al. 2005, Marzal et al. 2008, Palinauskas et al. 2011), and co-speciation (Garamszegi 2006, Perez-Tris et al. 2007, Ricklefs and Outlaw 2010, Loiseau et al. 2011), as well as individual effects of infection on host performance (Marzal et al. 2005, Spencer et al. 2005, Knowles et al. 2010, Asghar et al. 2011). Because haemosporidians often impact host fitness and ecological dynamics, they are also important in regards to conservation (van Riper III et al. 1986, Castro et al. 2011).

Avian haemosporidians can also have indirect effects on host communities in addition to the direct effects listed above. For example, Marzal et al. (2011) found that house sparrows Passer domesticus; HOSP, one of the world's most successful introduced vertebrate species (Summers-Smith 1988, Anderson 2006), had lost all eleven of its native European haemosporidian lineages during introduction to North and South America, and had become infected with only four generalist lineages from the introduced range (Marzal et al. 2011). Prevalence too was significantly lower in introduced relative to native populations of HOSP. As such, enemy release, defined as a reduction in parasite diversity and/or prevalence upon or after introduction, may have contributed to the broad distribution of HOSP (Torchin and Mitchell 2004, Catford et al. 2009). Enemy release from both specialist (species-specific) and generalist (broad-host range) parasites can occur either by chance, due to a bottleneck of host and parasite diversity (i.e. ‘missing the boat’), or because the parasites are lost on arrival if, for example, specialist parasites brought to the new range lack competent vectors or intermediate hosts. Enemy release may also occur if introduced hosts are not preferred by or competent for parasites or their vectors in the new range (Dobson and May 1986, Colautti et al. 2004). It is assumed that enemy release benefits invaders because resources can be reallocated from parasite defense to other pro-invasive physiological and behavioral processes (Blossey and Notzold 1995, Colautti et al. 2004). Although reduced parasite diversity is common in introduced species, evidence for reduced prevalence in introduced species is inconsistent (Cornell and Hawkins 1993, Torchin et al. 2002, 2003, Liu and Stiling 2006).

If enemy release facilitates introduction success, similar patterns should exist over smaller increments of space and time, such as during range expansions. Enemy release over the course of range expansions has been observed previously in diverse taxa (e.g. plants, invertebrates, and vertebrates) (Menéndez et al. 2008, Phillips et al. 2010, Flory et al. 2011). In these cases, serial introduction events, low host density, poor host competence, and/or selection for individuals with strong immunity at range edges have been invoked to explain why parasite diversity and prevalence is diminished (Phillips et al. 2010). On the other hand, high parasite prevalence could benefit some introduced populations. This concept, ‘novel weapons’, posits that if introduced hosts are less affected by a parasite than native hosts, the introduced host could out-compete native hosts by increasing the prevalence of that infectious agent in the native population (Callaway and Ridenour 2004, Kelly et al. 2009). Novel weapons could be carried by an introduced host during initial colonization (‘parasite spillover’) or may have been endemic but rare in the introduced range prior to the arrival of the introduced species (‘parasite spillback’)(Kelly et al. 2009).

For arthropod-vectored parasites such as avian haemo sporidians, it could be difficult to assess the strength the effect of range expansion has on processes such as enemy release and novel weapons because host–parasite dynamics are dependent on a variety of other factors. For example, temperature and moisture (including seasonal variation) are often associated with increased prevalence of haemosporidian infection, presumably because of effects on the re-emergence of latent infections in hosts as well as effects on vector abundance and activity (Valkiūnas 2005, Bonneaud et al. 2009, Lachish et al. 2011a, Sehgal et al. 2011). At the individual level, host sex (Zuk and Stoehr 2002), age (Lavoie 2006), and/or reproductive effort (Korpimäki et al. 1993, Knowles et al. 2009, Isaksson et al. 2013) also affect probability of infection by a variety of parasites.

Here, using HOSP introduced to Kenya, we aim to address which factors have the greatest impact on risk of haemosporidian infections during an ongoing avian range expansion. Because enemy release predicts that reduced parasite prevalence facilitates establishment, such as is assumed to occur if enemy release is beneficial, haemosporidian prevalence should be lower among HOSP at the range edge. On the other hand, if HOSP establishment success is facilitated by increasing haemosporidian prevalence in native community members (i.e. novel weapons), then prevalence should be highest at the range edge – or perhaps, equal along the invasion gradient. By surveying the prevalence of haemosporidian infections in HOSP in more than ten sites along an invasion gradient in Kenya, we examined whether enemy release or novel weapons occurs along the invasion gradient or whether the effects of altitude, local climate and individual host traits better predicted infection risk.

Material and methods

  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References


HOSP were introduced to Mombasa (Fig. 1) in the early 1950s, likely from South Africa (Anderson 2006). Within 10 yr, HOSP had spread north along the coast to Malindi and inland to Voi, and subsequently established in Nairobi, to the northwest of Mombasa, sometime between the mid-1980s and mid-1990s (Lewis and Pomeroy 1989). Since then, HOSP have dispersed westward, reaching Moi's Bridge and Kakamega, near the Ugandan border in the early to mid-2000s (Martin et al. unpubl.). Because HOSP are non-migratory (Anderson 2006), we consider each population to be a different age, with the youngest populations occurring in the west and the oldest occurring in the east of Kenya (Schrey et al. 2011, Martin et al. unpubl.).


Figure 1. A map of Kenya labeled with the 12 cities where sampling of house sparrows occurred: (1) Mombasa, (2) Malindi, (3) Watamu, (4) Voi, (5) Garsen, (6) Nairobi, (7) Nyeri, (8) Nanyuki, (9) Isiolo, (10) Nakuru, (11) Kakamega, and (12) Moi's Bridge. House sparrows were introduced to Mombasa in the 1950s and subsequently advanced north-westward towards the Ugandan border. For population level analyses Moi's Bridge was dropped (low sample size) and data from Malindi and Watamu were combined. Map courtesy of < >.

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Sample collection

House sparrows were captured from 12 Kenyan cities (Fig. 1, Table 1) in July, October or November 2010, using mist-nets. Peak haemosporidian prevalence in neighboring Uganda is in April and October, corresponding with the middle and beginning of the two rainy seasons that occur from March to May and October to November, respectively (Bennett et al. 1974). Approximately 20 μl of blood was obtained by pricking the brachial vein with a sterile 26-gauge needle and then collected in heparinized microcapillary tubes. One drop (˜ 5 μl) of blood was used to create a blood smear; the remainder was stored in RNAlater and kept at room temperature for up to one month, after which samples were frozen at −40°C until DNA extraction. Individual body mass (to 0.1 g), age (juvenile or adult, based on plumage and beak characteristics), sex (house sparrows are sexually dimorphic at maturity), tarsus (to 0.1 mm), and wing chord length (to 1 mm) were recorded for nearly every individual.

Table 1. Sampling of Kenyan house sparrows for avian haemosporidians. The total number of individual that were samples and the number of infected individuals (with percent positive, i.e. prevalence, in parentheses) in each city, which are numbered as in Fig. 1. For population level analyses Moi's Bridge was dropped (low sample size) and data from Malindi and Watamu were combined.
CityCapture monthTotal sampledInfected individuals
1. MombasaJuly468 (17%)
2. MalindiJuly75 (71%)
3. WatamuJuly213 (14%)
4. VoiJuly211 (5%)
5. GarsenNovember143 (21%)
6. NairobiJuly3018 (60%)
7. NyeriOctober316 (19%)
8. NanyukiOctober192 (11%)
9. IsioloOctober212 (10%)
10. NakuruJuly305 (17%)
11. KakamegaJuly2010 (50%)
12. Moi's BridgeJuly40 (0%)

DNA extraction and PCR

Kenyan house sparrows are known to be exposed to and infected by several haemosporidian lineages (Bennett and Herman 1976, Bensch et al. 2009, Marzal et al. 2011). Although we recognize that different haemosporidian lineages could have variable effects on different avian hosts (between and within host species and perhaps even within host individuals), these data is currently unavailable for nearly all bird species, including Kenyan HOSP. Regardless, haemosporidian infections generally have negative fitness consequences for their passerine hosts (Marzal et al. 2005, Palinauskas et al. 2008, Knowles et al. 2009, Lachish et al. 2011b). Therefore overall haemosporidian prevalence should be a meaningful metric for monitoring population level processes, such as enemy release or novel weapons. To test our hypotheses it was more important to determine haemosporidian prevalence rather than to identify haemosporidian lineage diversity. As such, we used PCR primers that target a fragment of the rRNA coding sequence of haemosporidians from all three genera, Plasmodium, Haemoproteus and Leucocytozoon, as opposed to the primers used by Marzal et al. (2011), which target the cytochrome b sequence of only Plasmodium and Haemoproteus species but the product of which can be sequenced to identify parasite lineage (Waldenström et al. 2004). The rRNA primer set is also more sensitive to low copy number than the cytochrome b primers (Fallon et al. 2003). Although this approach can reduce the resolution of data for specialist haemosporidian parasites, it is the appropriate approach to determine general patterns of haemosporidian prevalence and their role (or lack thereof) in introduction success of HOSP.

DNA was extracted from the blood-RNAlater mixture using a standard phenol:chloroform:isoamyl alcohol (25:24:1) protocol with a NaCl-Tris purification (Barker 2005). The PCR protocol was based on that published by Fallon et al. (2003). Briefly, we used 10 μM primers 343F (5′GCTCACGCATCGCTTCT) and 496R (5′GACCGGTCATTTTCTTTG), 100 ng DNA, and PCR Master Mix to a final volume of 25 μl. Thermocycling conditions were: denaturing at 94°C for 2 min, then 45 cycles of 94°C for 50 s, 55°C for 50 s, and 72°C for 25 s, and a final elongation period at 72°C for 2 min. DNA extracted from known haemosporidian-positive birds was used as a positive control and ultrapure water and extracted Escherichia coli DNA as negative controls; all three controls were run between every 13 experimental samples (Waldenström et al. 2004). PCR products were run on 1% agarose gels with 0.01% ethidium bromide. Samples with single bands at approximately 150 bp (exact amplicon size is 153 bp) were considered positive. We attempted to identify haemosporidian species from blood smears of PCR-positive individuals but for only a fraction of PCR-positives was burden high enough to be detected by blood smear, so species identity was not used in any analyses and is not reported. In smears where parasites were found, all haemosporidians were identified as Plasmodium relictum based on morphology and parasite species distribution (Valkiūnas 2005).


We used model selection via AICc scores to investigate the relative importance of a variety of individual and population level variables on avian haemosporidian infections in Kenyan HOSP (Knowles et al. 2011, Isaksson et al. 2013). To examine individual-level predictors, we used generalized linear mixed models (GLMM) with a binomial distribution for our dependent variable (haemosporidian infection status, positive or negative), a logit link function, a robust covariance matrix, and Satterthwaite approximation of degrees of freedom, to account for variable sample sizes in each city (Table 1). Our full, individual- level model, from which we started backwards selection, had city as a random factor and mass, sex and tarsus as fixed effects; we did not include age or wing chord length because both were significantly correlated with other variables included in the model. Our second model selection procedure examined population level predictors. We again used GLMMs with a binomial error distribution for our dependent variable (haemosporidian infection status, positive or negative), a logit link function, a robust covariance matrix, and Satterthwaite approximation of degrees of freedom, but here we blocked individuals by city. For these analyses, we combined Malindi and Watamu data due to their very close proximity (˜ 18 km) and excluded Moi's Bridge data due to low sample size (n = 4). In our full population-level model we included altitude (m), average annual precipitation (cm; data from < > and National Museums of Kenya), capture month, and distance from Mombasa, a proxy of population age (km) (Schrey et al. 2011, Liebl and Martin 2012, Martin et al. unpubl), as fixed effects. Temperature was not included because we could not obtain temperature data from several of the rural locations we sampled. However, for the five cities for which we found temperature data (Mombasa, Malindi, Nairobi, Nakuru and Kakamega, NCDC 2010), altitude and minimum annual average temperature (R =−0.96), maximum annual average temperature (R =−0.97), and average annual average temperature (R =−0.98) were strongly correlated.

For both the individual and population level models, variables with p > 0.1 were removed using backwards selection. Model selection was based on second-order (corrected) Akaike information criteria (AICc) scores because of low sample sizes. We calculated ΔAICc for each model by subtracting the AICc value of the best model (lowest AICc) from its own AICc value. The relative likelihood of a given model versus the best model was calculated with the following formula: relative likelihood = Exp((−ΔAICc)/2) (Burnham and Anderson 2004). All statistics were run in SPSS ver. 21.0 and all figures were made in GraphPad Prism ver. 5.04.


  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

The best model for our individual-level analyses of haemosporidian prevalence included only body mass, with larger individuals having a greater probability of infection (Table 2, Fig. 2; t-test: p = 0.0257). The next best model had a ΔAICc of −38. For this reason, we did not calculate model weights nor perform model averaging which is typically suggested for models with ΔAICc < 2 (Burnham and Anderson 2004). Dropping city as a random factor from individual-level models greatly reduced AICc scores (Table 2).

Table 2. Summary of all models examining the best predictors of probability of infection at the individual level in HOSP. Fixed effects: (1) sex (male/female, juveniles excluded), (2) mass, and (3) tarsus length. Because it was not significant in any initial model, city was dropped as a random factor; the p-value is listed for the models that included a random effect of city. Model weights were not calculated because no model was within 2 ΔAICc units of the best model.
Fixed effectsRandom effect of city (p-value)ΔBICΔAICcLikelihood
2 00 
1 + 2 + 3 43.94638.4684.43 × 10−9
2 + 3 73.01369.8526.79 × 10−16
1 + 2 + 30.160918.539922.0905.90 × 10−201
2 + 30.160924.347927.8853.24 × 10−202
20.1161003.181006.6252.60 × 10−219

Figure 2. Probability of infection is positively correlated with mass. Infected individuals were significantly heavier than uninfected individuals (t-test: p = 0.0257). Numbers over the bars indicate the number of infected and uninfected individuals. Whiskers indicate minimum and maximum mass, the top and bottom of the box mark the upper and lower quartiles, and the mid-line indicates the mean for each group.

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The best population-level model for haemosporidian prevalence included average annual precipitation, distance from Mombasa, and capture month, although none of the variables were significant in this model (Table 3). Besides being in the best model, capture month was also the last remaining predictor in the model during backwards selection. Cities sampled in July (Mombasa, Malindi/Watamu, Voi, Nairobi, Nakuru and Kakamega) had higher prevalence than those sampled in October (Nanyuki, Nyeri and Isiolo) or November (Garsen) (Fig. 3a). When we performed binary logistic regression with infection status (positive/negative) as the dependent and capture month as the predictor, the model was marginally non-significant (p = 0.061) indicating that capture month was not a significant predictor alone. Despite their importance in the best model, neither precipitation (Fig. 3b; linear regression, blocked by city: 1/slope = 613.1, r2= 0.137, p = 0.327) nor distance from Mombasa (as a proxy of population age; Fig. 4) was independently correlated with infection prevalence, although their coefficients suggest that they, like sampling in July, are positively correlated with risk of infection. As above, because the next best model had a ΔAICc value of −17.880 we did not calculate model weights.

Table 3. Summary of all models examining the best predictors of probability of infection at the population level in HOSP and the fixed coefficients table for the best model (model with the lowest AICc). Fixed effects: (1) altitude, (2) average annual precipitation, (3) distance from Mombasa as an approximation of population age, and (4) month of sampling. Model weights were not calculated because no model was within 2 ΔAICc units of the best model.
Fixed effectsΔBICΔAICcLikelihood
2 + 3 + 400 
1 + 2 + 3 + 4−17.880−17.8857650.299
4−36.575−36.5158.5 × 107
3 + 4−51.870−51.8141.8 × 1011
 Best model   
Model termCoefficientExp(coefficient)SEtp
(2) Precipitation0.0021.0020.007−0.3530.774
(3) Population age0.0021.0020.001−1.4360.392
(4) Sampling month (July)1.1903.2880.670−1.7770.218

Figure 3. Both capture month and average annual precipitation were in the best model of haemosporidian prevalence. (a) Populations that were sampled in July had higher haemosporidian infection prevalence than those cities sampled in October or November, though differences were only marginally significant (p = 0.061). Percentages over the bars denote percent infected. (b) Precipitation and prevalence were not statistically correlated (p = 0.327).

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Figure 4. Prevalence of avian haemosporidian infections across the introduction gradient, with cities to the right (farther from Mombasa, the site of introduction) being increasingly younger. Prevalence does not follow a pattern that would be predicted by enemy release, though distance from Mombasa was included in the best model of population prevalence. City is identified below each bar and sample sizes are listed above each bar.

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  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

At the individual level, larger birds were more likely to be infected with haemosporidians (Fig. 1). None of the population-level factors significantly predicted infection risk, which suggests that characteristics of malaria infection prevalence in Kenyan house sparrows are complex. Capture month, average annual precipitation, and population age (distance from Mombasa) all remained in the best model for infection prevalence, but no factor alone strongly predicted population prevalence (Table 3). Below, we interpret these unexpected patterns and discuss their implications for range expansion in HOSP and related species.

Individual level factors: body mass

We expected body mass would either be lower in infected individuals, consistent with experimental Plasmodium (SGS1) infections of HOSP (Palinauskas et al. 2008), or be unrelated to haemosporidian infection as was the case in North and South American populations of HOSP (Martin et al. 2007). However, our finding that larger birds were more likely to be infected has precedence: in a massive study of malaria infections of European passerines (14 812 individuals belonging to 74 species in 39 genera in 17 families), Scheuerlein and Ricklefs (2004) found that besides host species identity, body mass was one of the most important predictors of haemosporidian infection status; as in our study, larger birds were more likely to be infected. A similar pattern was also found in a smaller study of southern Missouri songbirds for Plasmodium and Haemoproteus parasites (Ricklefs et al. 2005). Why this pattern occurs is still unresolved. It may be that heavier birds are more attractive to vectors (Suom et al. 2010) due to increased chemical attractants (Takken and Kline 1989), such as carbon dioxide (Brady et al. 1997). Alternatively or additionally, heavier birds might have higher encounter rates with mosquitoes due to particular behaviors (e.g. increased foraging at mosquito-dense sites) or by chance because of greater surface area (Atkinson and Van Riper III 1991), which in turn increases the risk of exposure.

Another possibility is that mass and immunity are related in HOSP. If this is the case, our positive correlation between mass and probability of infection may be explained in one of two ways. First, larger sparrows may invest more resources in growth and less in immunity, making them more susceptible to haemosporidian infections (Sheldon and Verhulst 1996, Lochmiller and Deerenberg 2000, Ricklefs and Wikelski 2002). Or, because haemosporidian infections have negative fitness consequences in many passerines (Atkinson et al. 2000, Merino et al. 2000, Valkiūnas 2005, Palinauskas et al. 2008, Knowles et al. 2009, 2010), it is feasible that the pattern is a product of differential survival rates among infected individuals: only birds with sufficient resources to maintain large body masses also have the resources to survive chronic haemosporidian infections. A study of Spanish HOSP supports this explanation; Navarro et al. (2003) found that larger birds mounted stronger immune responses than smaller birds. Future studies comparing these hypotheses could reveal why larger birds are more likely to be infected.

Population-level factors

We predicted that population age would be an important risk factor at the population level. Though there are assumptions regarding the effect of change in haemosporidian prevalence on a invasion success (Colautti et al. 2004), we expected that the mechanisms that often permit enemy release (e.g. reduced host population density, preference of vectors for native species) and novel weapons (e.g. differential response to parasite infection) could still operate and benefit introduced HOSP along the invasion gradient. Lower prevalence in more recently colonized locations would result if establishment was promoted by enemy release. High or similar prevalence along the invasion gradient (from old to young populations) could result if novel weapons promoted establishment. In this latter scenario, the cost of parasitism would be outweighed by the benefits gained from competitive advantage over native passerines. As predicted, population age was included in the best model and the coefficient suggests a slightly positive relationship between distance from Mombasa and prevalence, a pattern which supports novel weapons. However, this pattern was neither obvious (Fig. 4) nor statistically significant (Table 3).

One possible explanation for the lack of evidence for both enemy release and novel weapons is that we missed the important period of invasion. For example, in the enemy release described in another successful, range- expanding vertebrate, the Australian cane toad Bufo marinus, reduced prevalence of their native lungworm Rhabdias pseudophaerocephala lasted only 2.5 yr on average before the lungworm recolonized cane toad populations (Phillips et al. 2010). Our youngest population likely arrived between 5 and 10 yr ago. As such, our model may be picking up a residual importance of population age that is no longer statistically or biologically important. Alternatively, because we know that Kenyan HOSP are exposed to and being infected by generalist haemosporidians (Bennett and Herman 1976, Bensch et al. 2009, Marzal et al. 2011) and that native congeners are present in this system (P. griseus and P. motitensis), Kenyan HOSP may be at a similar risk of infection at all parts of their introduced range.

Capture month had a much stronger effect on infection risk than either distance from Mombasa or precipitation, but even this factor was non-significant. Interestingly, variation in prevalence with capture month is more likely attributable to seasonal fluctuations in host physiology (Martin et al. 2008) than to vector abundance or activity. Specifically, in our study, prevalence was highest in populations sampled in July (as compared to October/November), yet vector abundance is likely to be highest in March/April/May and October/November, coincident with the rainy seasons and appropriate weather conditions for vectors. A peak in prevalence in July also does not match up with previous findings by Bennett et al. (1974) who reported a major peak in haemosporidian prevalence in October and a smaller peak in April in native Ugandan birds. Some procedural disparities could explain why we found different patterns than Bennett and co-workers. First, Bennett et al. used blood smears to diagnose infections, which underestimate prevalence relative to PCR (Richard et al. 2002). Second, the Bennett et al. study did not include any species from Passeridae, the family to which HOSP belong. Yet, the most likely explanation for the differences in timing of peak prevalence involves specific differences in host–, parasite– or vector– species interactions. Previous studies in our laboratory suggest that HOSP may have physiological, genetic and epigenetic traits that impact range expansion success (Martin et al. 2010, Liebl and Martin 2012, 2013, Schrey et al. 2012). Such adaptations may affect other behavioral and physiological processes including parasite exposure and response.

Similar to distance from Mombasa's pattern of statistical non-significance but inclusion in the best model, average annual precipitation at capture location was not significantly correlated with haemosporidian prevalence in HOSP despite its inclusion in the best model (Fig. 3a). Typically, probability of haemosporidian infection is positively correlated with precipitation and proximity to water bodies (Foley et al. 2003, Lachish et al. 2011a). These results are normally attributed to the fact that haemosporidians are vectored by arthropods whose reproduction and population density is precipitation- and water-body dependent (Bennett et al. 1974, Chandler et al. 1977, Minakawa et al. 2002). Perhaps because the majority of our samples were collected between the two major rainy seasons (Table 1, Methods), when birds were still breeding (pers. obs. LBM, CACC), infected birds in our study were a mix of both new infections acquired in the previous 2–4 months, and relapsed, previously latent infections acquired during a previous rainy season or before (Applegate and Beaudoin 1970, Applegate 1971). Such a mixture would obscure a strong signal of precipitation on prevalence.

Our interest in patterns of prevalence led us to choose a methodology that did not permit identification of specific parasite species or lineage. Consequently, there are limitations on the conclusions we can draw. For example, though both this study and that by Marzal et al. found evidence of only Plasmodium relictum infections in Kenyan HOSP, there could be other haemosporidian species infecting HOSP which were not detected. Because different lineages of haemosporidians can have effects on passerines that are species- and/or individual-specific, we cannot presume the level of parasite virulence (Valkiūnas 2005). In fact, variation in haemosporidian species or lineages between populations and/or parasite–environment interactions (Chasar et al. 2009) may explain the variation we found between populations (Fig. 4). Also, without knowing the identity of the parasite strain (or the exact source population of HOSP) we cannot know the parasite's origin and without this information we cannot make conclusive statements about the loss of parasite diversity, or the possibility of parasite spillback (Kelly et al. 2009), parasite spillover (Callaway and Ridenour 2004), and/or co-introduction of one or more species of haemosporidians with HOSP (Ewen et al. 2012).

Regardless of the roles of the risk factors or identity of haemosporidians infecting HOSP, it is interesting that despite infection, in some cases at high levels of prevalence (Fig. 4), HOSP are still able to establish themselves in new areas in Kenya. Indeed, an alternative hypothesis to enemy release and tangential to the idea of novel weapons involves parasite tolerance: house sparrows may be successful because they can maintain fitness even when infected by haemosporidians or other parasites. Indeed, when (native) European HOSP are challenged with the same P. relictum lineage found in Kenya (SGS1) (Marzal et al. 2011), they do not lose mass, engage fever, or change hematocrit levels (Palinauskas et al. 2008). This lack of response is not abnormal in HOSP. In North America, HOSP did not exhibit strong sickness behaviors in response to simulated infections with diverse antigens (Coon et al. 2011) and they also maintained reproductive output after simulated infections better than a less successful introduced congener, the Eurasian tree sparrow (P. montanus) (Lee et al. 2005). Moreover, Kenyan HOSP gain body mass when their immune systems are stimulated whereas a native congener (P. motitensis) loses body mass (Martin et al. 2010). If HOSP are as unaffected by haemosporidians as they are with simulated infections, then haemosporidian prevalence in Kenyan populations should be primarily predicted by patterns of exposure rather than population age, as is the case here.


We found little evidence of enemy release or use of novel weapons by range-expanding HOSP in Kenya. Instead, haemosporidian prevalence was best predicted by characteristics that likely affect vectors at both the individual and population levels (i.e. rainfall and host size). It may be that host qualities, such as HOSP predisposition towards minimal behavioral and physiological response to parasites, may be more important to HOSP range expansion success than parasitological phenomena such as enemy release or novel weapons. On the other hand, more specialized, virulent and/or directly transmitted parasites might be less affected by environmental input and thus better fit a pattern of enemy release or novel weapons. A comparison of the roles of a variety of parasite guilds among diverse range expanding host taxa would be worthy of investigation, especially given that many hosts and parasites will undergo climate change induced range shifts in the coming century.


  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

We thank Andrea Liebl, Titus Imboma and Nico Nalianya for help collecting samples; Elizabeth Andreassi, Chris Caruana and Sara McLaughlin for help with DNA extraction; Robert Ricklefs for sharing avian haemosporidian positive DNA samples for PCR controls; and Brittany Sears, Amber Brace, Martyna Boruta, Cris Ledon-Rettig, Andrea Liebl, Jeb Byers, Dan Ardia, Jason Rohr, Holly Kilvitis, Alfonso Marzal and Javier Pérez-Tris for discussion of data and suggestions regarding the interpretation of results. All procedures were approved by the Institutional Animal Care and Use Committee of the Univ. of South Florida (W3202) and the Kenyan Ministry of Science and Technology. This work was funded by the Graduate Women in Science (CACC), Sigma Xi (CACC), the American Ornithologists Union (CACC), and NSF-IOS 0920475 (LBM).


  1. Top of page
  2. Abstract
  3. Material and methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  • Anderson T. R. 2006. Biology of the ubiquitous house sparrow. – Oxford Univ. Press.
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