Predictors of malaria infection in a wild population: Landscape level analyses reveal anthropogenic effects. Catalina Gonzalez-Quevedo a,* , Richard G Davies a , David S Richardson a a School of Biological Sciences, University of East Anglia, Norwich Research Park; Norwich, UK * Corresponding author: [email protected]1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
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Predictors of malaria infection in a wild population: Landscape level
analyses reveal anthropogenic effects.
Catalina Gonzalez-Quevedoa,*, Richard G Daviesa, David S Richardsona
a School of Biological Sciences, University of East Anglia, Norwich Research Park; Norwich, UK
The relative importance of natural abiotic, natural biotic, anthropogenic abiotic, and anthropogenic
biotic predictors in influencing prevalence of malaria was assessed using both non-spatial binomial
generalised linear models (GLM), and spatial autologistic models (Augustin, Mugglestone & Buckland
1996). We implemented model selection approaches (Burnham & Anderson 2001) to compare the
relative fit of competing models, or sets of models, using Akaike’s information criterion (AIC) as the
measure of model fit. We performed three sets of modelling procedures, nested within each of our
two modelling methods (non-spatial binomial GLMs and spatial autologistic models), one for each
buffer radius (50m, 100m, and 200m), hence performing a sensitivity analysis of potential scale-
dependent effects of buffer radius on our results. For each of our three model sets, distance based
environmental variables (DISTWATER, DISTCONST, DISTFARM, and DISTPOUL) remained invariant.
The results obtained at these three sampling scales were very similar (supplementary table S1),
therefore we chose to report only the results using the 100-m radius buffer, since this best
approximates the territory size of Berthelot’s pipit (Juan Carlos Illera Pers Comm.).
In each of our three model sets, nested within our two modelling methods, the same series of
modelling steps were repeated. First we compared AICs for single-predictor models, where each
predictor is tested separately. Prior to running multi-predictor models, co-linearity between each
pair of predictor variables was evaluated using pairwise bivariate correlations in PASW Statistics
version 18 (SPSS Inc. 2009, Chicago, IL, www.spss.com). When a pair of variables had a correlation
coefficient > 0.7, the variable with the highest single-predictor AIC (lowest fit) was dropped from our
set of predictors. We then ran all possible combinations of 9 predictors resulting in 511 models in
total and recorded the AIC, AIC (the difference between the best model’s AIC and that of the model
in question) and Akaike weights (a measure of the relative explanatory value of the model,
compared to all possible ones). We considered models with AIC ≤ 2 as having sufficient support
(Burnham & Anderson 2004). All possible model subsets that included biotic, abiotic, anthropogenic
and natural predictors were additionally assessed to determine which of the four environmental
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categories had most influence on malaria infection and in order to identify the best-fit model within
each subset.
For our binomial generalised linear models (GLM), we checked for spatial autocorrelation in the
model residuals by calculating Moran’s I coefficients at 1000 m distance classes and generating a
correlogram using the package ncf in R (Bjornstad 2012). Autologistic regression modelling
(Augustin, Mugglestone & Buckland 1996) was implemented to account for the observed spatial
autocorrelation by including an autocovariate to assume spatial autocorrelation up to a maximum of
1000 m. This autocovariate was calculated following Dormann et al. (2007) using the R package
spdep (Bivand 2012). Residual spatial autocorrelation was found to be absent from these autologistic
models. The R package fmsb (Nakazawa 2012) was used to calculate the Nagelkerke R2, a logistic
analog of R2 in ordinary regression.
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Results
PATHOGEN SCREENING
In total 388 Berthelot’s pipits were sampled between January and April 2011. Malaria was detected
in 156 out of 388 individuals (40.2%). Of these 156 individuals, 14 (9%) were infected with
Leucocytozoon, while Plasmodium was detected in 148 (95%), with six birds (3.8%) infected with
both genera. Three strains of Plasmodium were detected; LK6 and LK5 -first described in the Lesser
kestrel (Falco naumanii) (Ortego et al. 2007) - were detected in 139 and seven individuals,
respectively, while KYS9 - first isolated from Culex pipiens mosquitoes (Inci et al. 2012)- was found in
two individuals. Two strains of Leucocytozoon were detected; REB11 in 12 individuals and ANBE1
(Spurgin et al. 2012) in two. To avoid confounding the analyses by including different genera/strains
of protozoa, only birds infected with the most commonly detected strain, Plasmodium LK6, which
accounted for 139 out of 156 (89.1%) of all infections, were included as infected in the analyses.
MODELS AND SPATIAL ANALYSES
ALT was highly correlated with PRECIP (Spearman’s rho = 0.838, p < 0.001), and with MINTEMP
(Spearman’s rho = -0.869, p < 0.001). PRECIP was also correlated with MINTEMP (Spearman’s rho = -
0.923, p < 0.001). Since these three predictors fall into the same natural abiotic category, PRECIP and
ALT were removed on the basis that MINTEMP had the lowest AIC of the three among single-
predictor models (Table 1). Single-predictor GLMs further showed that DISTWATER followed by
MINTEMP and DISTPOUL best predicted malarial infection in pipits. Autologistic models resulted in a
better fit, but the relative importance of predictors remained the same (Table 1). In general, there
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was a relatively low amount of variation explained by each predictor as revealed by the Nagelkerke
R2 values (0.085 - 0.139). In the following sections only the results from the spatial autologistic
models are presented.
In the multiple-predictor spatial models, the best fit model contained DISTWATER and DISTPOUL,
both being negatively correlated with the presence of malaria (Table 2). In 12 other models, all of
which contained DISTWATER and DISTPOUL, AIC was less than or equal to 2. Of the other
predictors VEGTYPE was present in five, while six other predictors were each present in three or less
of the best fit models. Out of 511 possible models, 263 models had a probability greater than 0.95 of
including the best model, indicating that the 95% candidate set was too broad to be informative.
Therefore we investigated the relative importance of each predictor individually, by calculating the
summed Akaike weight of all possible models where the predictor is present. Models containing
DISTWATER had a summed Akaike weight of 0.86; DISTPOUL had a summed Akaike weight of 0.81;
and MINTEMP had a summed Akaike weight of 0.44. The remaining five predictors had summed
Akaike weights lower than 0.41 (Table 3).
The results of all possible autologistic models representing different categories of environmental
variables (biotic, abiotic, anthropogenic and natural) are summarised in table 4. The best fit model
(lowest AIC) was the anthropogenic model containing DISTWATER and DISTPOUL which explained
16% of variation in malarial infection, followed by the abiotic model that included MINTEMP and
DISTWATER and the natural model with only MINTEMP. The predictor set with the least fit was the
biotic model including DISTPOUL and VEGTYPE.
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Discussion
By measuring variables at a fine landscape scale in an avian malaria-host system, we found evidence
that specific environmental factors influenced the distribution of malaria in a wild population. While
the climatic variables we analysed were not keypredictors of the prevalence of Plasmodium LK6 in
pipits in Tenerife, anthropogenic factors, such as distance to artificial bodies of water and distance to
poultry farms were.
Abiotic natural factors have been shown to play a major role in the prevalence and transmission of
vector-borne diseases (Sleeman et al. 2009; Linthicum et al. 2010), including malaria (Van Riper et
al. 1986; LaPointe, Goff & Atkinson 2010; Xiao et al. 2010; Sehgal et al. 2011). However, contrary to
predictions based on previous studies, and earlier work on pipits indicating a very low prevalence of
malaria at high altitudes (> 1600 m, Spurgin et al. 2012), temperature and altitude were not the best
predictors of malaria in the present study. MINTEMP was not present in the best fit multi-predictor
model and the combined Akaike weight of models containing MINTEMP (0.44) was low compared to
the values obtained for DISTWATER (0.86) and DISTPOUL (0.81). Why this is the case we are not
sure. It is possible that minimum temperatures are not sustained long enough for vectors and/or
parasites to be affected. Another possible explanation is that malaria transmission occurs only
during the warm summer months, so minimum temperature (which occurs in the winter months)
wouldn’t have an effect on the overall prevalence of the disease. Other abiotic natural variables that
have been identified as predictors of malaria are the topographic variables of aspect and slope,
which affect the presence and persistence of the wet habitats required for vector productivity (Balls
et al. 2004; Githeko et al. 2006; Cohen et al. 2008; Atieli et al. 2011). However, in the present study
we found no indication that either slope or aspect was important for malaria prevalence. This could
be due to the volcanic nature of soils in Tenerife (Fernandez-Caldas, Tejedor-Salguero & Quantin
1982), which are highly permeable and unlikely to hold water long enough to allow for larvae
development.
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Malaria prevalence has often been shown to be closely correlated with precipitation levels (Galardo
et al. 2009; Zhou et al. 2010; Bomblies 2012), but this is not the case in our study. In Tenerife rainfall
is scarce and the land is steep and porous, consequently water does not naturally remain in pools
long enough to provide habitats for vector reproduction. There are, however, many artificial water
pools and small canals that have been created for agricultural and other purposes. Thatdistance to
the nearest reservoir is a predictor of malaria infection while rainfall is not, suggests that these
reservoirs provide suitable habitats for vector larvae development thus facilitating malaria
transmission. Previous studies have shown artificial water reservoirs, irrigation canals, and dams to
be important for the production of malaria vectors (Fillinger et al. 2004) and that water bodies are
closely associated with malaria (Wood et al. 2007; Zhou et al. 2010; Lachish et al. 2011).
Various biotic factors may also influence the prevalence of malaria. Vegetation type did not have a
effect on malaria infection in our dataset. Berthelot’s pipits inhabit open areas and are not found in
closed canopy forests, such as the laurisilva present in the wetter northern parts of the island.
Hence, the low apparent importance of habitat may be a result of focusing on a specific host species,
rather than a pattern general to avian malaria. Nevertheless, it is also possible that the vectors of
malaria in Tenerife are not so much constrained by the vegetation cover and might be equally
abundant across areas.
Host density is also expected to affect malaria prevalence because it modifies vector-host contact
rates. However, while some studies support this prediction (Ortego & Cordero 2010), others,
including the present study, fail to find a correlation (Bonneaud et al. 2009). It may be that our
estimate of pipit density, based on the presence/absence of pipits in a square Kilometre grid is not
sufficiently accurate. This indirect measure was designed to reflect density at the appropriate scale,
however it is possible that pipit density is more highly localised than thought. Furthermore, Although
adult pipits tend to hold the same breeding territories from year to year (Illera & Diaz 2008), we do
not know whether the spatial structure of pipit density is constant year-round. Patterns of
movement and juvenile dispersal, which are not taken into consideration by the present study, could
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also have important implications for the transmission of infectious diseases such as malaria, (Altizer,
Oberhauser & Brower 2000; Jones et al. 2011). Direct measures of host density may provide a better
estimate of the effects that pipit density has on malaria prevalence; however, such measures would
be extremely difficult and time consuming to calculate, requiring counts of abundance within each
km2 across the year.
The local density of all vertebrate hosts, not just the focal host species, could have an effect on
malaria prevalence. Within host communities, some species act as key hosts harbouring parasitic
fauna, thus altering prevalence in other host species (Hellgren et al. 2011). The malaria strain
detected in the pipits, Plasmodium spp. LK6, has also been reported in blackbirds and canaries
(Phillips 2009), species that co-occur with pipits in many areas of Tenerife. Unfortunately, we have
no data on densities of these two species in order to investigate whether they have an effect on pipit
malaria prevalence. A comprehensive study of the prevalence of avian malaria in the bird
community on Tenerife would be needed to understand the role other bird species might play in the
prevalence of malaria.
Human activities have been shown to affect vector-borne diseases (Patz et al. 2000; Friggens & Beier
2010; Gottdenker et al. 2011), including malaria (Serandour et al. 2007). Factors such as
deforestation, animal husbandry, construction and artificial water management modify the
ecological balance within which vectors and their parasites develop and transmit disease (Patz et al.
2000). Livestock farms have been shown to influence the transmission of infectious diseases by
facilitating atypical aggregations of wild birds including infected individuals both of the focal, and
other species (Carrete et al. 2009). Conversely, such farms have also been shown to dilute the effect
of malaria transmission by reducing biting rates on susceptible hosts (Mutero et al. 2004; Liu et al.
2011). In the present study, distance from the nearest poultry farm at which a pipit was caught had a
significant negative effect on the probability of malaria infection. This suggests either that; i) poultry
farms provide suitable habitats for vectors, ii) aggregations of wild birds that facilitate transmission
occur as a result of these farms, or iii) poultry are themselves reservoirs of malaria. That there was
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no effect of distance to nearest non-poultry livestock farm, suggests the third explanation is most
plausible, as all livestock farms should equally support vectors and encourage bird aggregations. The
specific LK6 lineage has not been reported in poultry (though screening for such lineages in poultry
has rarely been undertaken), but various other Plasmodium lineages have been reported in both
poultry and wild birds (Bensch, Hellgren & Perez-Tris 2009), thus providing evidence that poultry can
be a reservoir of avian malaria. Direct screening of poultry farms on Tenerife would be needed to
confirm the association.
Other anthropogenic activities, such as urbanisation, can be important predictors of vector-borne
diseases (Bradley & Altizer 2007) including malaria (Guthmann et al. 2002). For example, mosquito
species can quickly adapt to urban environments (Antonio-Nkondjio et al. 2011; Kamdem et al.
2012) and urbanisation has been shown to increase human malaria prevalence (Alemu et al. 2011).
In our analyses, distance to the nearest constructed site had a negative effect on malaria prevalence
in the individual predictor non-spatial model, however, the effect disappeared after accounting for
spatial autocorrelation. It may well be that the broad classification of ‘urbanisation’ used in this
study lacks the resolution to identify the particular characteristics of urbanisation that influence
malaria prevalence. Given that urban expansion is happening around the world, further work to
understand its impact on wildlife disease prevalence is warranted.
While our models have identified key environmental variables associated with malaria infection,
considerable variation (83%) remains unexplained. This confirms the view that wildlife diseases, such
as malaria, are complex and that many different factors, including ones not closely linked to
environmental gradients, can influence their spatial distribution within a host population. (Hawley &
Altizer 2011). It is especially important to note that host related factors, such as individual immunity
(and immune variation in the population), host movement patterns and stochastic processes that
might influence the epidemiology of malaria were not accounted for in this study. Furthermore, as
the specific vectors that transmit avian malaria in Tenerife have not yet been described (but see
Bensch, Hellgren & Perez-Tris 2009), we were unable to incorporate an understanding of the ecology
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of these vectors into our analysis. Finally, although DISTWATER did predict the prevalence of
malaria, the resolution of the GIS layer used in our study was not able to capture the presence of
very small water bodies that might be equally important in malaria transmission. Consequently we
may have underestimated the explanatory power of this predictor.
Assessing the role of the environment in the transmission of pathogens in the wild is crucial to our
understanding of disease dynamics and of the causes and consequences of host-pathogen
coevolution. The evidence from our study supports previous work which suggests that the
prevalence of malaria can vary over small spatial scales (Lachish et al. 2011). Contrary to other
studies which found that climatic variables were strongly associated with malaria prevalence (Balls
et al. 2004; Briet, Vounatsou & Amerasinghe 2008; Garamszegi 2011), we found that anthropogenic
environmental variables, namely proximity to artificial water reservoirs and poultry farms, were the
most important predictors of malaria in pipits across Tenerife. This may, at least in part, reflect the
scale at which the study was performed – when measured across greater scales the influence of
locally important predictors of disease may be swamped by regional differences (Balls et al. 2004;
Briet, Vounatsou & Amerasinghe 2008; Garamszegi 2011). This study demonstrates the importance
of measuring local fine scale variation, and not just regional effects, in order to understand how
environmental variation can influence wildlife diseases.
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Acknowledgements
The research was funded by a PhD grant to CGQ from Colciencias and by the School of Biological
Sciences of the University of East Anglia. Fieldwork was partly funded by the John and Pamela Salter
Trust. We would like to thank the Spanish government for providing the permits to work in Tenerife;
H. Bellman, J.C. Illera, D. Padilla, L. Spurgin and K. Phillips for field assistance and J.C. Illera, I. Melo,
J.M. Ochoa and L. Spurgin for comments on the manuscript.
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