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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
* Corresponding author: [email protected]
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Summary
1. How the environment influences the transmission and maintenance of disease in a population of
hosts is a key aspect of disease ecology. The role that environmental factors play in host-pathogen
systems has been well studied at large scales, i.e. differences in pathogen pressures among separate
populations of hosts, or across land masses. However, despite considerable understanding of how
environmental conditions vary at fine spatial scales, the effect of these parameters on host-
pathogen dynamics at such scales has been largely overlooked.
2. Here we used a combination of molecular screening and GIS-based analysis to investigate how
environmental factors determine the distribution of malaria across the landscape in a population of
Berthelot’s pipit (Anthus berthelotii) in Tenerife.
3. Anthropogenic factors were better predictors of malaria distribution than natural factors in this
population, with proximity to water reservoirs and poultry farms being the key predictors of
infection. Contrary to predictions, climatic differences across the landscape did not appear to
influence the distribution of malaria. This may reflect the scale at which the study was performed.
4. These results suggest that levels of malaria infection in this endemic species are artificially
elevated by the impact of humans.
5. Studies such as the one described here improve our understanding of how environmental factors,
and their heterogeneity, affect the distribution of pathogens within wild populations. The results
demonstrate the importance of measuring fine scale variation, and not just regional effects, in order
to understand how environmental variation can influence wildlife diseases. Such understanding is
important for predicting the future spread and impact of disease and may help inform disease
management programmes, as well as the conservation of specific host species.
Key words: Avian malaria, Berthelot’s pipit, environmental predictors, GLM, model selection
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Introduction
Understanding how ecological variables influence the prevalence and transmission of disease in a
population is a key issue in ecology (Hudson 2002). This is especially important at a time when
climate and anthropogenic habitat changes are dramatically affecting the distribution of pathogens
and their hosts (Harvell et al. 2002; Lafferty & Gerber 2002; Garamszegi 2011), and the number of
emerging infectious diseases of wildlife is increasing (Daszak 2000). Establishing how biotic and
abiotic factors influence the distribution of disease is important if we are to predict the spatial
patterns of future disease threats and effectively manage their impact on biodiversity (Murray et al.
2011). Some recent studies have assessed how environmental variables affect pathogen distribution
across landscapes in wild populations (e.g. Kleindorfer & Dudaniec 2006; Wood et al. 2007; Murray
et al. 2011) and identified relationships have been used to predict where pathogens are likely to
survive and/or disease outbreaks to arise (Ron 2005; Lerdthusnee et al. 2008; Murray et al. 2011).
However, such studies have normally been done at coarse scales (but see Eisen & Wright 2001 and
Wood et al. 2007 for exceptions), thus the effects of fine scale environment variation on the
distribution of pathogens has been largely overlooked. Studies at finer spatial scales are now
required to provide insight into why pathogens typically have patchy distributions within a landscape
(Eisen & Wright 2001; Salje et al. 2012).
Haemosporidian parasites, such as species of the genera Plasmodium, Heamoproteous and
Leucocytozoon (hereafter termed malaria for simplicity), are intracellular protozoan blood parasites
transmitted by blood sucking invertebrates that occur in every continent apart from Antarctica
(Valkiunas 2005). In humans, malaria is a major public health problem with more than 200 million
cases and more than one million deaths each year (Chuang & Richie 2012). Malaria also infects other
vertebrates including reptiles, turtles, birds and other mammals, reducing their survival and fitness
(Martinsen, Perkins & Schall 2008).
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The vector-transmitted nature of malaria and the need for specific conditions to complete parasite
development means that transmission, and therefore patterns of infection, are highly dependent on
environmental factors (Guthmann et al. 2002). A number of key abiotic variables can affect the
distribution of malaria (Lapointe, Atkinson & Samuel 2012). Temperature has a considerable effect
(Xiao et al. 2010; Sehgal et al. 2011) because sub-optimal temperatures are associated with slower
parasite development (Valkiunas 2005; LaPointe, Goff & Atkinson 2010) and reductions in vector
density (Gilioli & Mariani 2011). Elevation influences malaria through its negative correlation with
temperature (Balls et al. 2004; LaPointe, Goff & Atkinson 2010; Lapointe, Atkinson & Samuel 2012).
Water availability is important because of the role it plays in vector larval development (Gilioli &
Mariani 2011) and rainfall has been shown to be correlated with malaria prevalence (Briet,
Vounatsou & Amerasinghe 2008; Galardo et al. 2009; Zhou et al. 2010), as has distance to bodies of
water independent of rainfall (Wood et al. 2007; Haque et al. 2009). In areas where precipitation is
low, topography may also play an important role in water accumulation and therefore vector
abundance (Balls et al. 2004; Clennon et al. 2010). Biotic variables, including host life-history traits
and demographic factors, may also affect the distribution and transmission of malaria (Ortego &
Cordero 2010; Gilioli & Mariani 2011). Malaria prevalence has been shown to be positively
associated with host density (Ortego & Cordero 2010), but negatively correlated with non-host
species density because of the dilution effect these have on the transmission of malaria (Mutero et
al. 2004; Nah, Kim & Lee 2010). Additionally, the presence of other diseases could make hosts more
susceptible to malaria by modifying behavior or by affecting host immune response (Van Riper et al.
1986; Jarvi et al. 2008).
The factors described above all relate to ‘natural’ environmental variables, but anthropogenic
activities such as animal husbandry and urbanisation can also affect the prevalence of malaria (Patz
et al. 2000). One such factor that has been little explored is the influence of livestock on the
transmission of malaria. If livestock serve as a reservoir of malaria, their presence might increase
prevalence within taxonomically similar local fauna - though this will depend on the host-specificity
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of the malaria strains involved (Yotoko & Elisei 2006; Beadell et al. 2009). On the other hand,
livestock could dilute malaria transmission by providing non-host blood meals to vectors (Nah, Kim &
Lee 2010). At another level, livestock farms could increase prevalence if they alter the local
environment, for example by providing water reservoirs suitable for vector development or by
facilitating transmission through aggregations of wild animals attracted to the farms. Urbanisation
may also have an impact on malaria prevalence, contingent on the degree of urban adaptation of
host species (Bradley & Altizer 2007), and on the extent to which such areas provide suitable
habitats for vector development (Guthmann et al. 2002; Omumbo et al. 2005; Antonio-Nkondjio et
al. 2011).
Assessing and comparing the roles of the environmental variables potentially influencing malaria
prevalence is challenging, and their importance may vary between different vector species, strains of
malaria, and hosts species. Avian malaria occurs in most bird species (Valkiunas 2005) and can have
a heterogeneous spatial distribution even within a single host population (Eisen & Wright 2001;
Wood et al. 2007; Lachish et al. 2011) Infection. infection can have negative effects on individual
fitness (Marzal et al. 2005; Marzal et al. 2008; Knowles, Palinauskas & Sheldon 2010), and is thought
to have contributed to the decline and extinction of several bird species (Van Riper et al. 1986). As
such, it represents an important disease threat to wild bird populations. Finally, because of their
relatively high density, distribution throughout the landscape (often occurring in both pristine and
anthropogenic habitats) and the ease with which they can be sampled, avian species provide
excellent models in which to investigate the causes and consequences of malaria transmission in
natural systems (Fallon et al. 2004; Ricklefs, Fallon & Bermingham 2004). .
Berthelot’s pipit (Anthus berthelotii) is a passerine endemic to the Macaronesian archipelagos. The
large and widespread population on Tenerife exists across a range of habitats and ecological
gradients, but is isolated from all other conspecific populations (Illera, Emerson & Richardson 2007).
Importantly it is host to significant levels of a restricted number of avian malaria strains (Illera,
Emerson & Richardson 2008; Spurgin et al. 2012) -thus making analyses tractable. This population,
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therefore, provides an excellent study system with which to assess how ecological and landscape
factors influence malaria prevalence in a wild population.
The aim of the present study was to use a combination of molecular disease screening of individuals
and fine scale GIS analysis of the habitats in which they were sampled to: (i) determine how the
environment modulates the prevalence of malaria, and (ii) assess the relative importance of
anthropogenic, natural, biotic and abiotic environmental factors, on the presence of malaria in pipits
across the landscape.
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Methods
STUDY SPECIES AND SAMPLING
Berthelot’s pipit (Anthus berthelotii) is a sedentary passerine that lives on all the islands in the
Atlantic archipelagos of the Canary Islands, Selvagens and Madeira (Cramp & Perrins 1977). On
Tenerife (Fig. 1) the pipit is numerous and widespread, inhabiting open, semi-arid habitats from sea
level up to mountainous habitats at elevations of 3700 m. To obtain a representative sample of the
pipit across its entire range and all environmental gradients on Tenerife, a 1 Km 2 grid was laid over a
map of the island obtained from Google Earth in ArcGIS version 10 (Esri 2011, Redlands, CA,
www.esri.com). Each accessible Km2 that contained habitat suitable for pipits was visited and
whether or not pipits were present was recorded; where present, an attempt was made to catch at
least one pipit per Km2 using clap nets baited with Tenebrio molitor larvae. Each captured bird was
ringed and morphological measurements (wing length, tarsus length, bill length, bill height, bill
width, head length and mass) taken. Blood samples were taken from all birds by brachial
venipuncture and stored in 100% ethanol in screw cap micro-centrifuge tubes at room temperature.
MOLECULAR PROCEDURES
Genomic DNA was extracted from blood using a salt extraction method (Richardson et al. 2001). The
sex of each bird was determined by polymerase chain reaction (PCR) as described in Griffiths et al.
(1998). Only DNA samples that successfully amplified the sex specific markers were used in the
malaria screening. To screen for avian malaria infection a nested PCR was used following
(Waldenstrom et al. 2004). An initial PCR amplified a 580 bp fragment of the cytochrome b gene of
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the Haemosporidian genome, then, nested PCRs were performed to amplify Haemoproteus,
Plasmodium or Leucocytozoon following the methods described in Illera, Emerson & Richardson
(2008). All samples were screened at least twice (3 times in the case of any ambiguous results) and
only samples that amplified twice and were verified as malaria through sequencing were taken to be
infections. PCR products were sequenced using BigDye terminator reaction kit (Perkin Elmer Inc.
Waltham, MA) and products were run on an automated sequencer (ABI PRISM 3700, Applied
Biosystems, Carlsbad, USA). Sequences were visually checked using FinchTV
(http://www.geospiza.com/finchtv/) and aligned using BIOEDIT version 7.0.9 (Hall 1999) to
sequences from the National centre for Biotechnology Information (NCBI) GenBank database and
the MalAvi database for avian malaria (Bensch, Hellgren & Perez-Tris 2009).
ENVIRONMENTAL VARIABLES
Environmental variables were selected and assigned to one of four different categories as follows:
(1) Natural abiotic: minimum temperature of the coldest month (MINTEMP), precipitation (PRECIP),
aspect (ASPECT), slope (SLOPE) and altitude (ALT), (2) Natural biotic; vegetation type (VEGTYPE) and
pipit density (DENSITY), (3) Anthropogenic abiotic; distance to water reservoirs (DISTWATER),
distance to urban site (DISTCONST) and distance to livestock farms (DISTFARM), and (4)
Anthropogenic biotic; distance to poultry farms (DISTPOUL). DISTFARM was calculated as an
alternative predictor to DISTPOUL to account for effects derived from farm characteristics, and not
the animals bred in them per se, while DISTPOUL was included to investigate the effects that birds
bred in these farms might have on the transmission of malaria.
All environmental variable calculations and resampling were carried out in ArcGIS version 10 and R
(R Development Core Team 2011). The environmental variables, MINTEMP, ALT, SLOPE, ASPECT,
PRECIP, VEGTYPE and DENSITY, were calculated within 50 m, 100 m and 200 m radii buffers around
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each point where a bird had been caught in order to carry out a sensitivity analysis into the scale-
dependence of their effects (see below). In each case an area-weighted mean was calculated within
each buffer. Climatic variables (MINTEMP and PRECIP) were obtained from the WorldClim database
(Hijmans et al. 2005) at a resolution of 30 arc seconds (1 Km). Topographic variables (ALT, SLOPE,
ASPECT) at a resolution of 90 m were calculated from digital elevation models obtained from the
Shuttle Radar Topography Mission Digital Elevation Database version 4.1 (Consortium for Spatial
Information, www.cgiar-csi.org). Vegetation data were obtained from GRAFCAN (Cartográfica de
Canarias S.A., www.grafcan.com (Del-Arco et al. 2006) and were used to calculate proportional areas
of each of five categories of VEGTYPE (forest, grass, shrub, rock associated and urban associated
vegetation) within each buffer and each bird was assigned the majority vegetation type within the
surrounding buffer.
Distance variables (DISTWATER, DISTCONST, DISTFARM, and DISTPOUL) were calculated by
overlaying the layer for pipit location points over polygon layers for: artificial water reservoirs, urban
areas; and the position, species and census of livestock farms from the government of Tenerife (Plan
de ordenamiento territorial, Cabildo de Tenerife, http://www.tenerife.es/planes/). For each variable
the ‘proximity’ tool of the analysis extension of ArcGIS 10 was used to calculate the distance to the
nearest relevant feature for the variable concerned.
An index of pipit density (DENSITY) was calculated as the number of pipits per square Kilometre,
based on our geo-referenced records of pipit presence, using the ‘density’ tool of the spatial analyst
extension in ArcGIS 10, with a neighbourhood size of 2500 m radius around the centre of each
square Kilometre sampling cell. This index was used to reflect the size of the subpopulation of pipits
found in the same area as the sampled pipit and thus to provide a measure of the local conspecific
host population available for malaria infection.
ANALYSES
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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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Figure legends
Figure 1. Map of Tenerife…
Figure 2. Plots of predicted probabilities derived from the logistic regression models of a) distance to
water reservoirs and b) distance to poultry farms with LK6 infection status as the response variable.
Histograms show frequency of healthy (lower end)and infected (upper end) individuals at each 100
m distance class.
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700
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724
Table 1. Summary of single-predictor generalised linear (non-spatial) and autologistic (spatial)
models for the effect of environmental variables on the presence of malaria in Berthelot’s pipits
(fitted as a binary response variable)). AIC, p-value, Nagelkerke R2 and rank order of predictors based
on AIC are shown.
Non-spatial models Spatial models
Predictor Coefficient R2 p-value AIC Rank Coefficient R2 p-value AIC Rank
ALT-0.001 0.057 <0.001 493.8 4 -0.001 0.118 0.005 477.3 4
PRECIP-0.005 0.052 <0.001 495.2 5 -0.004 0.109 0.006 480.1 6
MINTEMP 0.201 0.077 <0.001 487.6 2 0.167 0.129 0.001 473.9 2
ASPECT-0.002 0.009 0.104 507.6 9 -0.002 0.088 0.244 486.6 9
SLOPE-0.034 0.006 0.194 508.5 10 -0.019 0.085 0.480 487.4 11
DISTWATER-0.001 0.080 <0.001 486.9 1 -0.001 0.139 0.002 470.6 1
DISTPOUL-1.4e-4 0.073 <0.001 489.1 3 -0.001 0.123 0.001 475.8 3
DISTFARM-0.3e-4 0.026 0.002 495.4 6 -3.1e-4 0.118 0.008 477.3 5
DIST_URB-0.001 0.026 0.028 502.9 7 -0.001 0.099 0.063 482.9 7
DENSITY 1.150 0.020 0.018 504.6 8 0.688 0.089 0.179 486.1 8
VEGTYPE 15.00 0.022 1.000 509.8 11 15.00 0.106 1.000 486.9 10
26
725
726
727
728
729
730
731
732
733
734
735
736
737
Table 2. Autologistic models with AIC ≤ 2 when compared to the best fit model out of the 511
possible models generated.
Model rank Predictors AIC AIC
1 DISTWATER + DISTPOUL 466.9 0
2 DISTWATER + DISTPOUL + VEGTYPE 467.1 0.2
3 MINTEMP + DISTWATER + DISTPOUL 467.6 0.7
4 DISTWATER + DISTPOUL + DIST_URB 468.4 1.5
5 DISTWATER + DISTPOUL + VEGTYPE + DIST_URB 468.5 1.6
6 ASPECT + DISTWATER + DISTPOUL 468.6 1.7
7 MINTEMP + DISTWATER + DISTPOUL + VEGTYPE 468.6 1.7
8 SLOPE + DISTWATER + DISTPOUL 468.7 1.8
9 DISTWATER + DISTPOUL + DISTFARM 468.8 1.9
10 MINTEMP + DISTWATER + DISTPOUL + DIST_URB 468.8 1.9
11 DISTWATER + DISTPOUL + VEGTYPE + DISTFARM 468.9 2
12 DISTWATER + DISTPOUL + DENSITY 468.9 2
13 ASPECT + DISTWATER + DISTPOUL + VEGTYPE 468.9 2
27
738
739
Table 3. Summed Akaike weights (wi) from all possible models that contain each predictor out of the
511 models generated.
Predictor wi Rank
DISTWATER 0.86 1
DISTPOUL 0.81 2
MINTEMP 0.44 3
VEGTYPE 0.41 4
DISTURB 0.33 5
ASPECT 0.32 6
DISTFARM 0.32 6
SLOPE 0.29 7
DENSITY 0.27 8
28
740
741
Table 4. Summary of best-fit multi-predictor autologistic models for the effect of each of four
environmental variable categories (biotic, abiotic, natural, and anthropogenic) on the presence of
malaria in Berthelot’s pipits. These categories of effect were assessed by comparing all possible
models containing variables for each category in turn. Coefficients and p-values of predictors, as well
as the AIC and Nagelkerke R2 of the best fit model for each category are shown.
Category Predictor Anthropogenic Natural Biotic Abiotic
Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value
AnthropogenicDISTWATE
R-7.60E-04 0.010 -0.001 0.037
DISTFARM - - - -
DIST_URB - - - -
DISTPOUL -7.75E-05 0.020 -1.20E-04 <0.001
Natural MINTEMP 0.167 0.001 0.093 0.163
ASPECT - - - -
SLOPE - - - -
DENSITY - - - -
VEGTYPE - - 15 1
AIC 470.1 473.9 475.5 470.5
R2 0.16 0.129 0.149 0.146
29
742
743
744
745
746
747
748
749
750
751
752
753
754
Supplementary table 1. Summary of single and multi-predictor autologistic models for the 50 m and
200 m radii for the sensitivity analyses.
50m radius 200m radiusModel Predictors AIC pval R2 Predictors AIC pval R2Altitude ALT 477.3 0.005 0.118 ALT 477.3 0.005 0.118Precipitation PRECIP 482.1 0.001 0.102 PRECIP 482.6 0.023 0.101Mintemp MINTEMP 473.8 0.001 0.129 MINTEMP 474.0 0.001 0.129Aspect ASPECT 481.1 0.010 0.106 ASPECT 480.6 0.008 0.107Slope SLOPE 487.4 0.447 0.085 SLOPE 487.2 0.387 0.085Water DISTWATER 470.5 0.002 0.139 DISTWATER 470.6 0.002 0.139Poultry DISTPOUL 475.8 0.001 0.123 DISTPOUL 475.8 0.001 0.123Farm DISTFARM 477.3 0.008 0.118 DISTFARM 477.3 0.008 0.118Density DENSITY 486.2 0.181 0.089 DENSITY 487.0 0.320 0.087Vegetation VEGTYPE 486.0 0.982 0.109 VEGTYPE 491.9 1 0.097Construction DIST_URB 482.9 0.063 0.100 DIST_URB 482.9 0.063 0.099Best fit anthropogenic DISTWATER
466.90.010
0.157DISTWATER
466.90.010
0.157DISTPOUL 0.020 DISTPOUL 0.020Best fit natural MINTEMP
469.00.001
0.151MINTEMP
469.40.002
0.150ASPECT 0.009 ASPECT 0.011Best fit biotic DISTPOUL 475.8 0.001 0.123 DISTPOUL 475.8 0.001 0.123Best fit abiotic ASPECT 464.2 0.004 0.167
ASPECT 464.1
0.004 0.166
DISTWATER 0.002 DISTWATER 0.002
30
755
756
757
