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Predictors of malaria infection in a wild population: Landscape level
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analyses reveal anthropogenic effects.
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Catalina Gonzalez-Quevedoa,*, Richard G Daviesa, David S Richardsona
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School of Biological Sciences, University of East Anglia, Norwich Research Park; Norwich, UK
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Corresponding author: C.gonzalez-quevedo@uea.ac.uk
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Summary
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1. How the environment influences the transmission and maintenance of disease in a population of
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hosts is a key aspect of disease ecology. The role that environmental factors play in host-pathogen
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systems has been well studied at large scales, i.e. differences in pathogen pressures among separate
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populations of hosts, or across land masses. However, despite considerable understanding of how
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environmental conditions vary at fine spatial scales, the effect of these parameters on host-pathogen
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dynamics at such scales has been largely overlooked.
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2. Here we used a combination of molecular screening and GIS-based analysis to investigate how
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environmental factors determine the distribution of malaria across the landscape in a population of
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Berthelot’s pipit (Anthus berthelotii) in Tenerife.
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3. Anthropogenic factors were better predictors of malaria distribution than natural factors in this
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population, with proximity to water reservoirs and poultry farms being the key predictors of infection.
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Contrary to predictions, climatic differences across the landscape did not appear to influence the
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distribution of malaria. This may reflect the scale at which the study was performed.
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4. These results suggest that levels of malaria infection in this endemic species are artificially elevated
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by the impact of humans.
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5. Studies such as the one described here improve our understanding of how environmental factors,
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and their heterogeneity, affect the distribution of pathogens within wild populations. The results
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demonstrate the importance of measuring fine scale variation, and not just regional effects, in order
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to understand how environmental variation can influence wildlife diseases. Such understanding is
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important for predicting the future spread and impact of disease and may help inform disease
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management programmes, as well as the conservation of specific host species.
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Key words: Avian malaria, Berthelot’s pipit, environmental predictors, GLM, model selection
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Introduction
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Understanding how ecological variables influence the prevalence and transmission of disease in a
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population is a key issue in ecology (Hudson 2002). This is especially important at a time when climate
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and anthropogenic habitat changes are dramatically affecting the distribution of pathogens and their
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hosts (Harvell et al. 2002; Lafferty & Gerber 2002; Garamszegi 2011), and the number of emerging
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infectious diseases of wildlife is increasing (Daszak 2000). Establishing how biotic and abiotic factors
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influence the distribution of disease is important if we are to predict the spatial patterns of future
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disease threats and effectively manage their impact on biodiversity (Murray et al. 2011). Some recent
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studies have assessed how environmental variables affect pathogen distribution across landscapes in
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wild populations (e.g. Kleindorfer & Dudaniec 2006; Wood et al. 2007; Murray et al. 2011) and
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identified relationships have been used to predict where pathogens are likely to survive and/or
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disease outbreaks to arise (Ron 2005; Lerdthusnee et al. 2008; Murray et al. 2011). However, such
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studies have normally been done at coarse scales (but see Eisen & Wright 2001 and Wood et al. 2007
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for exceptions), thus the effects of fine scale environment variation on the distribution of pathogens
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has been largely overlooked. Studies at finer spatial scales are now required to provide insight into
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why pathogens typically have patchy distributions within a landscape (Eisen & Wright 2001; Salje et
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al. 2012).
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Haemosporidian parasites, such as species of the genera Plasmodium, Heamoproteous and
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Leucocytozoon (hereafter termed malaria for simplicity), are intracellular protozoan blood parasites
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transmitted by blood sucking invertebrates that occur in every continent apart from Antarctica
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(Valkiunas 2005). In humans, malaria is a major public health problem with more than 200 million
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cases and more than one million deaths each year (Chuang & Richie 2012). Malaria also infects other
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vertebrates including reptiles, turtles, birds and other mammals, reducing their survival and fitness
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(Martinsen, Perkins & Schall 2008).
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The vector-transmitted nature of malaria and the need for specific conditions to complete parasite
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development means that transmission, and therefore patterns of infection, are highly dependent on
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environmental factors (Guthmann et al. 2002). A number of key abiotic variables can affect the
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distribution of malaria (Lapointe, Atkinson & Samuel 2012). Temperature has a considerable effect
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(Xiao et al. 2010; Sehgal et al. 2011) because sub-optimal temperatures are associated with slower
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parasite development (Valkiunas 2005; LaPointe, Goff & Atkinson 2010) and reductions in vector
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density (Gilioli & Mariani 2011). Elevation influences malaria through its negative correlation with
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temperature (Balls et al. 2004; LaPointe, Goff & Atkinson 2010; Lapointe, Atkinson & Samuel 2012).
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Water availability is important because of the role it plays in vector larval development (Gilioli &
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Mariani 2011) and rainfall has been shown to be correlated with malaria prevalence (Briet, Vounatsou
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& Amerasinghe 2008; Galardo et al. 2009; Zhou et al. 2010), as has distance to bodies of water
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independent of rainfall (Wood et al. 2007; Haque et al. 2009). In areas where precipitation is low,
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topography may also play an important role in water accumulation and therefore vector abundance
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(Balls et al. 2004; Clennon et al. 2010). Biotic variables, including host life-history traits and
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demographic factors, may also affect the distribution and transmission of malaria (Ortego & Cordero
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2010; Gilioli & Mariani 2011). Malaria prevalence has been shown to be positively associated with host
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density (Ortego & Cordero 2010), but negatively correlated with non-host species density because of
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the dilution effect these have on the transmission of malaria (Mutero et al. 2004; Nah, Kim & Lee
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2010). Additionally, the presence of other diseases could make hosts more susceptible to malaria by
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modifying behavior or by affecting host immune response (Van Riper et al. 1986; Jarvi et al. 2008).
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The factors described above all relate to ‘natural’ environmental variables, but anthropogenic
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activities such as animal husbandry and urbanisation can also affect the prevalence of malaria (Patz et
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al. 2000). One such factor that has been little explored is the influence of livestock on the transmission
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of malaria. If livestock serve as a reservoir of malaria, their presence might increase prevalence within
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taxonomically similar local fauna - though this will depend on the host-specificity of the malaria strains
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involved (Yotoko & Elisei 2006; Beadell et al. 2009). On the other hand, livestock could dilute malaria
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transmission by providing non-host blood meals to vectors (Nah, Kim & Lee 2010). At another level,
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livestock farms could increase prevalence if they alter the local environment, for example by providing
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water reservoirs suitable for vector development or by facilitating transmission through aggregations
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of wild animals attracted to the farms. Urbanisation may also have an impact on malaria prevalence,
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contingent on the degree of urban adaptation of host species (Bradley & Altizer 2007), and on the
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extent to which such areas provide suitable habitats for vector development (Guthmann et al. 2002;
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Omumbo et al. 2005; Antonio-Nkondjio et al. 2011).
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Assessing and comparing the roles of the environmental variables potentially influencing malaria
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prevalence is challenging, and their importance may vary between different vector species, strains of
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malaria, and hosts species. Avian malaria occurs in most bird species (Valkiunas 2005) and can have a
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heterogeneous spatial distribution even within a single host population (Eisen & Wright 2001; Wood
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et al. 2007; Lachish et al. 2011) Infection. infection can have negative effects on individual fitness
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(Marzal et al. 2005; Marzal et al. 2008; Knowles, Palinauskas & Sheldon 2010), and is thought to have
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contributed to the decline and extinction of several bird species (Van Riper et al. 1986). As such, it
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represents an important disease threat to wild bird populations. Finally, because of their relatively
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high density, distribution throughout the landscape (often occurring in both pristine and
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anthropogenic habitats) and the ease with which they can be sampled, avian species provide excellent
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models in which to investigate the causes and consequences of malaria transmission in natural
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systems (Fallon et al. 2004; Ricklefs, Fallon & Bermingham 2004). .
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Berthelot’s pipit (Anthus berthelotii) is a passerine endemic to the Macaronesian archipelagos. The
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large and widespread population on Tenerife exists across a range of habitats and ecological gradients,
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but is isolated from all other conspecific populations (Illera, Emerson & Richardson 2007). Importantly
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it is host to significant levels of a restricted number of avian malaria strains (Illera, Emerson &
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Richardson 2008; Spurgin et al. 2012) -thus making analyses tractable. This population, therefore,
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provides an excellent study system with which to assess how ecological and landscape factors
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influence malaria prevalence in a wild population.
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The aim of the present study was to use a combination of molecular disease screening of individuals
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and fine scale GIS analysis of the habitats in which they were sampled to: (i) determine how the
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environment modulates the prevalence of malaria, and (ii) assess the relative importance of
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anthropogenic, natural, biotic and abiotic environmental factors, on the presence of malaria in pipits
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across the landscape.
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Methods
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STUDY SPECIES AND SAMPLING
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Berthelot’s pipit (Anthus berthelotii) is a sedentary passerine that lives on all the islands in the Atlantic
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archipelagos of the Canary Islands, Selvagens and Madeira (Cramp & Perrins 1977). On Tenerife (Fig.
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1) the pipit is numerous and widespread, inhabiting open, semi-arid habitats from sea level up to
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mountainous habitats at elevations of 3700 m. To obtain a representative sample of the pipit across
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its entire range and all environmental gradients on Tenerife, a 1 Km2 grid was laid over a map of the
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island obtained from Google Earth in ArcGIS version 10 (Esri 2011, Redlands, CA, www.esri.com). Each
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accessible Km2 that contained habitat suitable for pipits was visited and whether or not pipits were
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present was recorded; where present, an attempt was made to catch at least one pipit per Km2 using
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clap nets baited with Tenebrio molitor larvae. Each captured bird was ringed and morphological
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measurements (wing length, tarsus length, bill length, bill height, bill width, head length and mass)
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taken. Blood samples were taken from all birds by brachial venipuncture and stored in 100% ethanol
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in screw cap micro-centrifuge tubes at room temperature.
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MOLECULAR PROCEDURES
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Genomic DNA was extracted from blood using a salt extraction method (Richardson et al. 2001). The
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sex of each bird was determined by polymerase chain reaction (PCR) as described in Griffiths et al.
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(1998). Only DNA samples that successfully amplified the sex specific markers were used in the malaria
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screening. To screen for avian malaria infection a nested PCR was used following (Waldenstrom et al.
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2004). An initial PCR amplified a 580 bp fragment of the cytochrome b gene of the Haemosporidian
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genome, then, nested PCRs were performed to amplify Haemoproteus, Plasmodium or Leucocytozoon
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following the methods described in Illera, Emerson & Richardson (2008). All samples were screened
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at least twice (3 times in the case of any ambiguous results) and only samples that amplified twice and
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were verified as malaria through sequencing were taken to be infections. PCR products were
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sequenced using BigDye terminator reaction kit (Perkin Elmer Inc. Waltham, MA) and products were
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run on an automated sequencer (ABI PRISM 3700, Applied Biosystems, Carlsbad, USA). Sequences
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were visually checked using FinchTV (http://www.geospiza.com/finchtv/) and aligned using BIOEDIT
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version 7.0.9 (Hall 1999) to sequences from the National centre for Biotechnology Information (NCBI)
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GenBank database and the MalAvi database for avian malaria (Bensch, Hellgren & Perez-Tris 2009).
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ENVIRONMENTAL VARIABLES
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Environmental variables were selected and assigned to one of four different categories as follows: (1)
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Natural abiotic: minimum temperature of the coldest month (MINTEMP), precipitation (PRECIP),
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aspect (ASPECT), slope (SLOPE) and altitude (ALT), (2) Natural biotic; vegetation type (VEGTYPE) and
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pipit density (DENSITY), (3) Anthropogenic abiotic; distance to water reservoirs (DISTWATER), distance
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to urban site (DISTCONST) and distance to livestock farms (DISTFARM), and (4) Anthropogenic biotic;
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distance to poultry farms (DISTPOUL). DISTFARM was calculated as an alternative predictor to
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DISTPOUL to account for effects derived from farm characteristics, and not the animals bred in them
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per se, while DISTPOUL was included to investigate the effects that birds bred in these farms might
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have on the transmission of malaria.
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All environmental variable calculations and resampling were carried out in ArcGIS version 10 and R (R
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Development Core Team 2011). The environmental variables, MINTEMP, ALT, SLOPE, ASPECT, PRECIP,
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VEGTYPE and DENSITY, were calculated within 50 m, 100 m and 200 m radii buffers around each point
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where a bird had been caught in order to carry out a sensitivity analysis into the scale-dependence of
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their effects (see below). In each case an area-weighted mean was calculated within each buffer.
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Climatic variables (MINTEMP and PRECIP) were obtained from the WorldClim database (Hijmans et al.
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2005) at a resolution of 30 arc seconds (1 Km). Topographic variables (ALT, SLOPE, ASPECT) at a
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resolution of 90 m were calculated from digital elevation models obtained from the Shuttle Radar
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Topography Mission Digital Elevation Database version 4.1 (Consortium for Spatial Information,
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www.cgiar-csi.org). Vegetation data were obtained from GRAFCAN (Cartográfica de Canarias S.A.,
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www.grafcan.com (Del-Arco et al. 2006) and were used to calculate proportional areas of each of five
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categories of VEGTYPE (forest, grass, shrub, rock associated and urban associated vegetation) within
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each buffer and each bird was assigned the majority vegetation type within the surrounding buffer.
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Distance variables (DISTWATER, DISTCONST, DISTFARM, and DISTPOUL) were calculated by overlaying
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the layer for pipit location points over polygon layers for: artificial water reservoirs, urban areas; and
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the position, species and census of livestock farms from the government of Tenerife (Plan de
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ordenamiento territorial, Cabildo de Tenerife, http://www.tenerife.es/planes/). For each variable the
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‘proximity’ tool of the analysis extension of ArcGIS 10 was used to calculate the distance to the nearest
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relevant feature for the variable concerned.
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An index of pipit density (DENSITY) was calculated as the number of pipits per square Kilometre, based
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on our geo-referenced records of pipit presence, using the ‘density’ tool of the spatial analyst
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extension in ArcGIS 10, with a neighbourhood size of 2500 m radius around the centre of each square
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Kilometre sampling cell. This index was used to reflect the size of the subpopulation of pipits found in
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the same area as the sampled pipit and thus to provide a measure of the local conspecific host
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population available for malaria infection.
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ANALYSES
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The relative importance of natural abiotic, natural biotic, anthropogenic abiotic, and anthropogenic
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biotic predictors in influencing prevalence of malaria was assessed using both non-spatial binomial
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generalised linear models (GLM), and spatial autologistic models (Augustin, Mugglestone & Buckland
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1996). We implemented model selection approaches (Burnham & Anderson 2001) to compare the
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relative fit of competing models, or sets of models, using Akaike’s information criterion (AIC) as the
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measure of model fit. We performed three sets of modelling procedures, nested within each of our
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two modelling methods (non-spatial binomial GLMs and spatial autologistic models), one for each
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buffer radius (50m, 100m, and 200m), hence performing a sensitivity analysis of potential scale-
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dependent effects of buffer radius on our results. For each of our three model sets, distance based
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environmental variables (DISTWATER, DISTCONST, DISTFARM, and DISTPOUL) remained invariant. The
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results obtained at these three sampling scales were very similar (supplementary table S1), therefore
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we chose to report only the results using the 100-m radius buffer, since this best approximates the
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territory size of Berthelot’s pipit (Juan Carlos Illera Pers Comm.).
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In each of our three model sets, nested within our two modelling methods, the same series of
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modelling steps were repeated. First we compared AICs for single-predictor models, where each
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predictor is tested separately. Prior to running multi-predictor models, co-linearity between each pair
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of predictor variables was evaluated using pairwise bivariate correlations in PASW Statistics version
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18 (SPSS Inc. 2009, Chicago, IL, www.spss.com). When a pair of variables had a correlation coefficient >
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0.7, the variable with the highest single-predictor AIC (lowest fit) was dropped from our set of
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predictors. We then ran all possible combinations of 9 predictors resulting in 511 models in total and
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recorded the AIC, AIC (the difference between the best model’s AIC and that of the model in question)
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and Akaike weights (a measure of the relative explanatory value of the model, compared to all possible
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ones). We considered models with AIC ≤ 2 as having sufficient support (Burnham & Anderson 2004).
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All possible model subsets that included biotic, abiotic, anthropogenic and natural predictors were
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additionally assessed to determine which of the four environmental categories had most influence on
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malaria infection and in order to identify the best-fit model within each subset.
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For our binomial generalised linear models (GLM), we checked for spatial autocorrelation in the model
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residuals by calculating Moran’s I coefficients at 1000 m distance classes and generating a correlogram
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using the package ncf in R (Bjornstad 2012). Autologistic regression modelling (Augustin, Mugglestone
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& Buckland 1996) was implemented to account for the observed spatial autocorrelation by including
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an autocovariate to assume spatial autocorrelation up to a maximum of 1000 m. This autocovariate
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was calculated following Dormann et al. (2007) using the R package spdep (Bivand 2012). Residual
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spatial autocorrelation was found to be absent from these autologistic models. The R package fmsb
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(Nakazawa 2012) was used to calculate the Nagelkerke R2, a logistic analog of R2 in ordinary regression.
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Results
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PATHOGEN SCREENING
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In total 388 Berthelot’s pipits were sampled between January and April 2011. Malaria was detected in
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156 out of 388 individuals (40.2%). Of these 156 individuals, 14 (9%) were infected with Leucocytozoon,
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while Plasmodium was detected in 148 (95%), with six birds (3.8%) infected with both genera. Three
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strains of Plasmodium were detected; LK6 and LK5 -first described in the Lesser kestrel (Falco naumanii)
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(Ortego et al. 2007) - were detected in 139 and seven individuals, respectively, while KYS9 - first
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isolated from Culex pipiens mosquitoes (Inci et al. 2012)- was found in two individuals. Two strains of
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Leucocytozoon were detected; REB11 in 12 individuals and ANBE1 (Spurgin et al. 2012) in two. To
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avoid confounding the analyses by including different genera/strains of protozoa, only birds infected
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with the most commonly detected strain, Plasmodium LK6, which accounted for 139 out of 156 (89.1%)
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of all infections, were included as infected in the analyses.
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MODELS AND SPATIAL ANALYSES
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ALT was highly correlated with PRECIP (Spearman’s rho = 0.838, p < 0.001), and with MINTEMP
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(Spearman’s rho = -0.869, p < 0.001). PRECIP was also correlated with MINTEMP (Spearman’s rho = -
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0.923, p < 0.001). Since these three predictors fall into the same natural abiotic category, PRECIP and
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ALT were removed on the basis that MINTEMP had the lowest AIC of the three among single-predictor
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models (Table 1). Single-predictor GLMs further showed that DISTWATER followed by MINTEMP and
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DISTPOUL best predicted malarial infection in pipits. Autologistic models resulted in a better fit, but
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the relative importance of predictors remained the same (Table 1). In general, there was a relatively
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low amount of variation explained by each predictor as revealed by the Nagelkerke R2 values (0.085 -
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0.139). In the following sections only the results from the spatial autologistic models are presented.
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In the multiple-predictor spatial models, the best fit model contained DISTWATER and DISTPOUL, both
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being negatively correlated with the presence of malaria (Table 2). In 12 other models, all of which
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contained DISTWATER and DISTPOUL, AIC was less than or equal to 2. Of the other predictors
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VEGTYPE was present in five, while six other predictors were each present in three or less of the best
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fit models. Out of 511 possible models, 263 models had a probability greater than 0.95 of including
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the best model, indicating that the 95% candidate set was too broad to be informative. Therefore we
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investigated the relative importance of each predictor individually, by calculating the summed Akaike
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weight of all possible models where the predictor is present. Models containing DISTWATER had a
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summed Akaike weight of 0.86; DISTPOUL had a summed Akaike weight of 0.81; and MINTEMP had a
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summed Akaike weight of 0.44. The remaining five predictors had summed Akaike weights lower than
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0.41 (Table 3).
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The results of all possible autologistic models representing different categories of environmental
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variables (biotic, abiotic, anthropogenic and natural) are summarised in table 4. The best fit model
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(lowest AIC) was the anthropogenic model containing DISTWATER and DISTPOUL which explained 16%
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of variation in malarial infection, followed by the abiotic model that included MINTEMP and
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DISTWATER and the natural model with only MINTEMP. The predictor set with the least fit was the
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biotic model including DISTPOUL and VEGTYPE.
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Discussion
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By measuring variables at a fine landscape scale in an avian malaria-host system, we found evidence
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that specific environmental factors influenced the distribution of malaria in a wild population. While
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the climatic variables we analysed were not keypredictors of the prevalence of Plasmodium LK6 in
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pipits in Tenerife, anthropogenic factors, such as distance to artificial bodies of water and distance to
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poultry farms were.
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Abiotic natural factors have been shown to play a major role in the prevalence and transmission of
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vector-borne diseases (Sleeman et al. 2009; Linthicum et al. 2010), including malaria (Van Riper et al.
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1986; LaPointe, Goff & Atkinson 2010; Xiao et al. 2010; Sehgal et al. 2011). However, contrary to
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predictions based on previous studies, and earlier work on pipits indicating a very low prevalence of
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malaria at high altitudes (> 1600 m, Spurgin et al. 2012), temperature and altitude were not the best
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predictors of malaria in the present study. MINTEMP was not present in the best fit multi-predictor
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model and the combined Akaike weight of models containing MINTEMP (0.44) was low compared to
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the values obtained for DISTWATER (0.86) and DISTPOUL (0.81). Why this is the case we are not sure.
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It is possible that minimum temperatures are not sustained long enough for vectors and/or parasites
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to be affected. Another possible explanation is that malaria transmission occurs only during the warm
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summer months, so minimum temperature (which occurs in the winter months) wouldn’t have an
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effect on the overall prevalence of the disease. Other abiotic natural variables that have been
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identified as predictors of malaria are the topographic variables of aspect and slope, which affect the
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presence and persistence of the wet habitats required for vector productivity (Balls et al. 2004;
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Githeko et al. 2006; Cohen et al. 2008; Atieli et al. 2011). However, in the present study we found no
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indication that either slope or aspect was important for malaria prevalence. This could be due to the
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volcanic nature of soils in Tenerife (Fernandez-Caldas, Tejedor-Salguero & Quantin 1982), which are
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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
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et al. 2009; Zhou et al. 2010; Bomblies 2012), but this is not the case in our study. In Tenerife rainfall
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is scarce and the land is steep and porous, consequently water does not naturally remain in pools long
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enough to provide habitats for vector reproduction. There are, however, many artificial water pools
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and small canals that have been created for agricultural and other purposes. Thatdistance to the
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nearest reservoir is a predictor of malaria infection while rainfall is not, suggests that these reservoirs
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provide suitable habitats for vector larvae development thus facilitating malaria transmission.
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Previous studies have shown artificial water reservoirs, irrigation canals, and dams to be important
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for the production of malaria vectors (Fillinger et al. 2004) and that water bodies are closely associated
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with malaria (Wood et al. 2007; Zhou et al. 2010; Lachish et al. 2011).
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Various biotic factors may also influence the prevalence of malaria. Vegetation type did not have a
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effect on malaria infection in our dataset. Berthelot’s pipits inhabit open areas and are not found in
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closed canopy forests, such as the laurisilva present in the wetter northern parts of the island. Hence,
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the low apparent importance of habitat may be a result of focusing on a specific host species, rather
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than a pattern general to avian malaria. Nevertheless, it is also possible that the vectors of malaria in
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Tenerife are not so much constrained by the vegetation cover and might be equally abundant across
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areas.
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Host density is also expected to affect malaria prevalence because it modifies vector-host contact
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rates. However, while some studies support this prediction (Ortego & Cordero 2010), others, including
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the present study, fail to find a correlation (Bonneaud et al. 2009). It may be that our estimate of pipit
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density, based on the presence/absence of pipits in a square Kilometre grid is not sufficiently accurate.
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This indirect measure was designed to reflect density at the appropriate scale, however it is possible
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that pipit density is more highly localised than thought. Furthermore, Although adult pipits tend to
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hold the same breeding territories from year to year (Illera & Diaz 2008), we do not know whether the
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spatial structure of pipit density is constant year-round. Patterns of movement and juvenile dispersal,
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which are not taken into consideration by the present study, could also have important implications
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for the transmission of infectious diseases such as malaria, (Altizer, Oberhauser & Brower 2000; Jones
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et al. 2011). Direct measures of host density may provide a better estimate of the effects that pipit
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density has on malaria prevalence; however, such measures would be extremely difficult and time
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consuming to calculate, requiring counts of abundance within each km2 across the year.
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The local density of all vertebrate hosts, not just the focal host species, could have an effect on malaria
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prevalence. Within host communities, some species act as key hosts harbouring parasitic fauna, thus
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altering prevalence in other host species (Hellgren et al. 2011). The malaria strain detected in the
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pipits, Plasmodium spp. LK6, has also been reported in blackbirds and canaries (Phillips 2009), species
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that co-occur with pipits in many areas of Tenerife. Unfortunately, we have no data on densities of
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these two species in order to investigate whether they have an effect on pipit malaria prevalence. A
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comprehensive study of the prevalence of avian malaria in the bird community on Tenerife would be
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needed to understand the role other bird species might play in the prevalence of malaria.
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Human activities have been shown to affect vector-borne diseases (Patz et al. 2000; Friggens & Beier
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2010; Gottdenker et al. 2011), including malaria (Serandour et al. 2007). Factors such as deforestation,
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animal husbandry, construction and artificial water management modify the ecological balance within
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which vectors and their parasites develop and transmit disease (Patz et al. 2000). Livestock farms have
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been shown to influence the transmission of infectious diseases by facilitating atypical aggregations
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of wild birds including infected individuals both of the focal, and other species (Carrete et al. 2009).
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Conversely, such farms have also been shown to dilute the effect of malaria transmission by reducing
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biting rates on susceptible hosts (Mutero et al. 2004; Liu et al. 2011). In the present study, distance
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from the nearest poultry farm at which a pipit was caught had a significant negative effect on the
368
probability of malaria infection. This suggests either that; i) poultry farms provide suitable habitats for
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vectors, ii) aggregations of wild birds that facilitate transmission occur as a result of these farms, or iii)
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poultry are themselves reservoirs of malaria. That there was no effect of distance to nearest non-
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poultry livestock farm, suggests the third explanation is most plausible, as all livestock farms should
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equally support vectors and encourage bird aggregations. The specific LK6 lineage has not been
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reported in poultry (though screening for such lineages in poultry has rarely been undertaken), but
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various other Plasmodium lineages have been reported in both poultry and wild birds (Bensch,
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Hellgren & Perez-Tris 2009), thus providing evidence that poultry can be a reservoir of avian malaria.
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Direct screening of poultry farms on Tenerife would be needed to confirm the association.
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Other anthropogenic activities, such as urbanisation, can be important predictors of vector-borne
378
diseases (Bradley & Altizer 2007) including malaria (Guthmann et al. 2002). For example, mosquito
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species can quickly adapt to urban environments (Antonio-Nkondjio et al. 2011; Kamdem et al. 2012)
380
and urbanisation has been shown to increase human malaria prevalence (Alemu et al. 2011). In our
381
analyses, distance to the nearest constructed site had a negative effect on malaria prevalence in the
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individual predictor non-spatial model, however, the effect disappeared after accounting for spatial
383
autocorrelation. It may well be that the broad classification of ‘urbanisation’ used in this study lacks
384
the resolution to identify the particular characteristics of urbanisation that influence malaria
385
prevalence. Given that urban expansion is happening around the world, further work to understand
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its impact on wildlife disease prevalence is warranted.
387
While our models have identified key environmental variables associated with malaria infection,
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considerable variation (83%) remains unexplained. This confirms the view that wildlife diseases, such
389
as malaria, are complex and that many different factors, including ones not closely linked to
390
environmental gradients, can influence their spatial distribution within a host population. (Hawley &
391
Altizer 2011). It is especially important to note that host related factors, such as individual immunity
392
(and immune variation in the population), host movement patterns and stochastic processes that
393
might influence the epidemiology of malaria were not accounted for in this study. Furthermore, as the
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specific vectors that transmit avian malaria in Tenerife have not yet been described (but see Bensch,
395
Hellgren & Perez-Tris 2009), we were unable to incorporate an understanding of the ecology of these
396
vectors into our analysis. Finally, although DISTWATER did predict the prevalence of malaria, the
397
resolution of the GIS layer used in our study was not able to capture the presence of very small water
17
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bodies that might be equally important in malaria transmission. Consequently we may have
399
underestimated the explanatory power of this predictor.
400
Assessing the role of the environment in the transmission of pathogens in the wild is crucial to our
401
understanding of disease dynamics and of the causes and consequences of host-pathogen coevolution.
402
The evidence from our study supports previous work which suggests that the prevalence of malaria
403
can vary over small spatial scales (Lachish et al. 2011). Contrary to other studies which found that
404
climatic variables were strongly associated with malaria prevalence (Balls et al. 2004; Briet, Vounatsou
405
& Amerasinghe 2008; Garamszegi 2011), we found that anthropogenic environmental variables,
406
namely proximity to artificial water reservoirs and poultry farms, were the most important predictors
407
of malaria in pipits across Tenerife. This may, at least in part, reflect the scale at which the study was
408
performed – when measured across greater scales the influence of locally important predictors of
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disease may be swamped by regional differences (Balls et al. 2004; Briet, Vounatsou & Amerasinghe
410
2008; Garamszegi 2011). This study demonstrates the importance of measuring local fine scale
411
variation, and not just regional effects, in order to understand how environmental variation can
412
influence wildlife diseases.
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Acknowledgements
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The research was funded by a PhD grant to CGQ from Colciencias and by the School of Biological
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Sciences of the University of East Anglia. Fieldwork was partly funded by the John and Pamela Salter
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Trust. We would like to thank the Spanish government for providing the permits to work in Tenerife;
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H. Bellman, J.C. Illera, D. Padilla, L. Spurgin and K. Phillips for field assistance and J.C. Illera, I. Melo,
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J.M. Ochoa and L. Spurgin for comments on the manuscript.
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References
Alemu, A., Tsegaye, W., Golassa, L. & Abebe, G. (2011) Urban malaria and associated risk factors in
Jimma town, south-west Ethiopia. Malaria Journal, 10.
Altizer, S.M., Oberhauser, K.S. & Brower, L.P. (2000) Associations between host migration and the
prevalence of a protozoan parasite in natural populations of adult monarch butterflies.
Ecological Entomology, 25, 125-139.
Antonio-Nkondjio, C., Fossog, B.T., Ndo, C., Djantio, B.M., Togouet, S.Z., Awono-Ambene, P., Costantini,
C., Wondji, C.S. & Ranson, H. (2011) Anopheles gambiae distribution and insecticide resistance
in the cities of Douala and Yaounde (Cameroon): influence of urban agriculture and pollution.
Malaria Journal, 10.
Arechavaleta, M., S. Rodríguez, N. Zurita & A. García (coord.) (2010) Lista de especies silvestres de
Canarias. Hongos, plantas y animales terrestres. 2009. (ed. G.d. Canarias), pp. 579. Gobierno
de Canarias, Santa Cruz de Tenerife.
Atieli, H.E., Zhou, G.F., Lee, M.C., Kweka, E.J., Afrane, Y., Mwanzo, I., Githeko, A.K. & Yan, G.Y. (2011)
Topography as a modifier of breeding habitats and concurrent vulnerability to malaria risk in
the western Kenya highlands. Parasites & Vectors, 4.
Augustin, N.H., Mugglestone, M.A. & Buckland, S.T. (1996) An autologistic model for the spatial
distribution of wildlife. Journal of Applied Ecology, 33, 339-347.
Balls, M.J., Bodker, R., Thomas, C.J., Kisinza, W., Msangeni, H.A. & Lindsay, S.W. (2004) Effect of
topography on the risk of malaria infection in the Usambara Mountains, Tanzania.
Transactions of the Royal Society of Tropical Medicine and Hygiene, 98, 400-408.
Beadell, J.S., Covas, R., Gebhard, C., Ishtiaq, F., Melo, M., Schmidt, B.K., Perkins, S.L., Graves, G.R. &
Fleischer, R.C. (2009) Host associations and evolutionary relationships of avian blood parasites
from West Africa. International Journal for Parasitology, 39, 257-266.
Bensch, S., Hellgren, O. & Perez-Tris, J. (2009) MalAvi: a public database of malaria parasites and
related haemosporidians in avian hosts based on mitochondrial cytochrome b lineages.
Molecular Ecology Resources, 9, 1353-1358.
Bivand, R. (2012) spdep: Spatial dependence: weighting schemes, statistics and models. R package
version 0.5-46. .
Bjornstad, O.N. (2012) ncf: spatial nonparametric covariance functions. R package version 1.1-4.
Bomblies, A. (2012) Modeling the role of rainfall patterns in seasonal malaria transmission. Climatic
Change, 112, 673-685.
Bonneaud, C., Sepil, I., Mila, B., Buermann, W., Pollinger, J., Sehgal, R.N.M., Valkiunas, G., Iezhova, T.A.,
Saatchi, S. & Smith, T.B. (2009) The prevalence of avian Plasmodium is higher in undisturbed
tropical forests of Cameroon. Journal of Tropical Ecology, 25, 439-447.
Bradley, C.A. & Altizer, S. (2007) Urbanization and the ecology of wildlife diseases. Trends in Ecology
&amp; Evolution, 22, 95-102.
Briet, O.J.T., Vounatsou, P. & Amerasinghe, P.H. (2008) Malaria seasonality and rainfall seasonality in
Sri Lanka are correlated in space. Geospatial Health, 2, 183-190.
Burnham, K.P. & Anderson, D.R. (2001) Model selection and multi-model inference: a practical
information–theoretic approach. Springer, New York, NY.
Burnham, K.P. & Anderson, D.R. (2004) Multimodel Inference, Understanding AIC and BIC in Model
Selection. Sociological methods and Research, 33, 261-304.
Carrete, M., Serrano, D., Illera, J.C., Lopez, G., Vogeli, M., Delgado, A. & Tella, J.L. (2009) Goats, birds,
and emergent diseases: apparent and hidden effects of exotic species in an island
environment. Ecological Applications, 19, 840-853.
Chuang, I. & Richie, T.L. (2012) World Malaria Report 2010: documenting progress towards malaria
eradication. Expert review of vaccines, 11, 39-41.
20
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
Clennon, J.A., Kamanga, A., Musapa, M., Shiff, C. & Glass, G.E. (2010) Identifying malaria vector
breeding habitats with remote sensing data and terrain-based landscape indices in Zambia.
International Journal of Health Geographics, 9.
Cohen, J.M., Ernst, K.C., Lindblade, K.A., Vulule, J.M., John, C.C. & Wilson, M.L. (2008) Topographyderived wetness indices are associated with household-level malaria risk in two communities
in the western Kenyan highlands. Malaria Journal, 7.
Cramp, S. & Perrins, C.M. (1977-1994) Handbook of the birds of Europe, the Middle East and Africa.
The birds of the western Palearctic. Oxford University Press, Oxford.
Daszak, P. (2000) Emerging infectious diseases of wildlife - Threats to biodiversity and human health
(vol 287, pg 443, 2000). Science, 287, 1756-1756.
Del-Arco, M., Perez-de-Paz, P.L., Acebes, J.R., Gonzalez-Mancebo, J.M., Reyes-Betancort, J.A., Bermejo,
J.A., de-Armas, S. & Gonzalez-Gonzalez, R. (2006) Bioclimatology and climatophilous
vegetation of Tenerife (Canary Islands). Annales Botanici Fennici, 43, 167-192.
Dormann, C.F., McPherson, J.M., Araujo, M.B., Bivand, R., Bolliger, J., Carl, G., Davies, R.G., Hirzel, A.,
Jetz, W., Kissling, W.D., Kuehn, I., Ohlemueller, R., Peres-Neto, P.R., Reineking, B., Schroeder,
B., Schurr, F.M. & Wilson, R. (2007) Methods to account for spatial autocorrelation in the
analysis of species distributional data: a review. Ecography, 30, 609-628.
Eisen, R.J. & Wright, N.M. (2001) Landscape features associated with infection by a malaria parasite
(Plasmodium mexicanum) and the importance of multiple scale studies. Parasitology, 122,
507-513.
Fallon, S.M., Ricklefs, R.E., Latta, S.C. & Bermingham, E. (2004) Temporal stability of insular avian
malarial parasite communities. Proceedings of the Royal Society of London Series B-Biological
Sciences, 271, 493-500.
Fernandez-Caldas, E., Tejedor-Salguero, M.L. & Quantin, P. (1982) Suelos de regiones volcánicas:
Tenerife. Viera y Clavijo, Santa Cruz de Tenerife.
Fillinger, U., Sonye, G., Killeen, G.F., Knols, B.G.J. & Becker, N. (2004) The practical importance of
permanent and semipermanent habitats for controlling aquatic stages of Anopheles gambiae
sensu lato mosquitoes: operational observations from a rural town in western Kenya. Tropical
Medicine & International Health, 9, 1274-1289.
Galardo, A.K.R., Zimmerman, R.H., Lounibos, L.P., Young, L.J., Galardo, C.D., Arruda, M. & Couto, A.
(2009) Seasonal abundance of anopheline mosquitoes and their association with rainfall and
malaria along the Matapi River, Amapi, Brazil. Medical and Veterinary Entomology, 23, 335349.
Garamszegi, L.Z. (2011) Climate change increases the risk of malaria in birds. Global Change Biology,
17, 1751-1759.
Gilioli, G. & Mariani, L. (2011) Sensitivity of Anopheles gambiae population dynamics to meteohydrological variability: a mechanistic approach. Malaria Journal, 10.
Githeko, A.K., Ayisi, J.M., Odada, P.K., Atieli, F.K., Ndenga, B.A., Githure, J.I. & Yan, G.Y. (2006)
Topography and malaria transmission heterogeneity in western Kenya highlands: prospects
for focal vector control. Malaria Journal, 5.
Griffiths, R., Double, M.C., Orr, K. & Dawson, R.J.G. (1998) A DNA test to sex most birds. Molecular
Ecology, 7, 1071-1075.
Guthmann, J.P., Llanos-Cuentas, A., Palacios, A. & Hall, A.J. (2002) Environmental factors as
determinants of malaria risk. A descriptive study on the northern coast of Peru. Tropical
Medicine & International Health, 7, 518-525.
Hall, T.A. (1999) BioEdit: a user friendly biological sequence alignment editor and analysis program for
Windows 95/98/NT. Nucleic Acid Symposium Series, 41, 95-98.
Haque, U., Huda, M., Hossain, A., Ahmed, S.M., Moniruzzaman, M. & Haque, R. (2009) Spatial malaria
epidemiology in Bangladeshi highlands. Malaria Journal, 8.
Harvell, C.D., Mitchell, C.E., Ward, J.R., Altizer, S., Dobson, A.P., Ostfeld, R.S. & Samuel, M.D. (2002)
Climate warming and disease risks for terrestrial and marine biota. Science, 296, 2158-2162.
21
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
Hawley, D.M. & Altizer, S.M. (2011) Disease ecology meets ecological immunology: understanding the
links between organismal immunity and infection dynamics in natural populations. Functional
Ecology, 25, 48-60.
Hellgren, O., Krizanauskiene, A., Hasselquist, D. & Bensch, S. (2011) Low haemosporidian diversity and
one key-host species in a bird malaria community on a mid-atlantic island (sao miguel, azores).
Journal of Wildlife Diseases, 47, 849-859.
Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated
climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978.
Hudson, P.J. (2002) The ecology of wildlife diseases. Oxford University Press, Oxford.
Illera, J.C. & Diaz, M. (2008) Site fidelity in the Canary Islands stonechat Saxicola dacotiae in relation
to spatial and temporal patterns of habitat suitability. Acta Oecologica-International Journal
of Ecology, 34, 1-8.
Illera, J.C., Emerson, B.C. & Richardson, D.S. (2007) Population history of Berthelot's pipit: colonization,
gene flow and morphological divergence in Macaronesia. Molecular Ecology, 16, 4599-4612.
Illera, J.C., Emerson, B.C. & Richardson, D.S. (2008) Genetic characterization, distribution and
prevalence of avian pox and avian malaria in the Berthelot's pipit (Anthus berthelotii) in
Macaronesia. Parasitology Research, 103, 1435-1443.
Inci, A., Yildirim, A., Njabo, K.Y., Duzlu, O., Biskin, Z. & Ciloglu, A. (2012) Detection and molecular
characterization of avian Plasmodium from mosquitoes in central Turkey. Veterinary
Parasitology, 188, 179-184.
Jarvi, S.I., Triglia, D., Giannoulis, A., Farias, M., Bianchi, K. & Atkinson, C.T. (2008) Diversity, origins and
virulence of Avipoxviruses in Hawaiian forest birds. Conservation Genetics, 9, 339-348.
Jones, E.O., Webb, S.D., Ruiz-Fons, F.J., Albon, S. & Gilbert, L. (2011) The effect of landscape
heterogeneity and host movement on a tick-borne pathogen. Theoretical Ecology, 4, 435-448.
Kamdem, C., Tene Fossog, B., Simard, F., Etouna, J., Ndo, C., Kengne, P., Bousses, P., Etoa, F.-X., AwonoAmbene, P., Fontenille, D., Antonio-Nkondjio, C., Besansky, N.J. & Costantini, C. (2012)
Anthropogenic Habitat Disturbance and Ecological Divergence between Incipient Species of
the Malaria Mosquito Anopheles gambiae. PloS one, 7, e39453.
Kleindorfer, S. & Dudaniec, R.Y. (2006) Increasing prevalence of avian poxvirus in Darwin's finches and
its effect on male pairing success. Journal of Avian Biology, 37, 69-76.
Knowles, S.C.L., Palinauskas, V. & Sheldon, B.C. (2010) Chronic malaria infections increase family
inequalities and reduce parental fitness: experimental evidence from a wild bird population.
Journal of Evolutionary Biology, 23, 557-569.
Lachish, S., Knowles, S.C.L., Alves, R., Wood, M.J. & Sheldon, B.C. (2011) Infection dynamics of endemic
malaria in a wild bird population: parasite species-dependent drivers of spatial and temporal
variation in transmission rates. Journal of Animal Ecology, 80, 1207-1216.
Lafferty, K.D. & Gerber, L.R. (2002) Good medicine for conservation biology: The intersection of
epidemiology and conservation theory. Conservation Biology, 16, 593-604.
Lapointe, D.A., Atkinson, C.T. & Samuel, M.D. (2012) Ecology and conservation biology of avian malaria.
Annals of the New York Academy of Sciences, 1249, 211-226.
LaPointe, D.A., Goff, M.L. & Atkinson, C.T. (2010) Thermal constraints to the sporogonic development
and altitudinal distribution of avian malaria plasmodium relictum in hawai'i. Journal of
Parasitology, 96, 318-324.
Lerdthusnee, K., Nigro, J., Monkanna, T., Leepitakrat, W., Leepitakrat, S., Insuan, S.,
Charoensongsermkit, W., Khlaimanee, N., Akkagraisee, W., Chayapum, K. & Jones, J.W. (2008)
Surveys of rodent-borne disease in Thailand with a focus on scrub typhus assessment.
Integrative Zoology, 3, 267-273.
Linthicum, K.J., Anyamba, A., Chretien, J.-P., Small, J., Tucker, C.J. & Britch, S.C. (2010) The Role of
Global Climate Patterns in the Spatial and Temporal Distribution of Vector-Borne Disease.
Springer, Po Box 17, 3300 Aa Dordrecht, Netherlands.
22
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
Liu, X.-B., Liu, Q.-Y., Guo, Y.-H., Jiang, J.-Y., Ren, D.-S., Zhou, G.-C., Zheng, C.-J., Zhang, Y., Liu, J.-L., Li,
Z.-F., Chen, Y., Li, H.-S., Morton, L.C., Li, H.-Z., Li, Q. & Gu, W.-D. (2011) The abundance and
host-seeking behavior of culicine species (Diptera: Culicidae) and Anopheles sinensis in
Yongcheng city, people's Republic of China. Parasites & Vectors, 4.
Loiseau, C., Harrigan, R.J., Robert, A., Bowie, R.C.K., Thomassen, H.A., Smith, T.B. & Sehgal, R.N.M.
(2012) Host and habitat specialization of avian malaria in Africa. Molecular Ecology, 21, 431441.
Martinsen, E.S., Perkins, S.L. & Schall, J.J. (2008) A three-genome phylogeny of malaria parasites
(Plasmodium and closely related genera): Evolution of life-history traits and host switches.
Molecular Phylogenetics and Evolution, 47, 261-273.
Marzal, A., Bensch, S., Reviriego, M., Balbontin, J. & de Lope, F. (2008) Effects of malaria double
infection in birds: one plus one is not two. Journal of Evolutionary Biology, 21, 979-987.
Marzal, A., de Lope, F., Navarro, C. & Moller, A.P. (2005) Malarial parasites decrease reproductive
success: an experimental study in a passerine bird. Oecologia, 142, 541-545.
Murray, K.A., Retallick, R.W.R., Puschendorf, R., Skerratt, L.F., Rosauer, D., McCallum, H.I., Berger, L.,
Speare, R. & VanDerWal, J. (2011) Assessing spatial patterns of disease risk to biodiversity:
implications for the management of the amphibian pathogen, Batrachochytrium
dendrobatidis. Journal of Applied Ecology, 48, 163-173.
Mutero, C.M., Kabutha, C., Kimani, V., Kabuage, L., Gitau, G., Ssennyonga, J., Githure, J., Muthami, L.,
Kaida, A., Musyoka, L., Marie, E. & Oganda, M. (2004) A transdisciplinary perspective on the
links between malaria and agroecosystems in Kenya. Acta Tropica, 89, 171-186.
Nah, K., Kim, Y. & Lee, J.M. (2010) The dilution effect of the domestic animal population on the
transmission of P. vivax malaria. Journal of Theoretical Biology, 266, 299-306.
Nakazawa, M. (2012) fmsb: Functions for medical statistics book with some demographic data. R
package version 0.3.4.
Omumbo, J.A., Hay, S.I., Snow, R.W., Tatem, A.J. & Rogers, D.J. (2005) Modelling malaria risk in East
Africa at high-spatial resolution. Tropical Medicine & International Health, 10, 557-566.
Ortego, J., Calabuig, G., Cordero, P.J. & Aparicio, J.M. (2007) Genetic characterization of avian malaria
(Protozoa) in the endangered lesser kestrel, Falco naumanni. Parasitology Research, 101,
1153-1156.
Ortego, J. & Cordero, P.J. (2010) Factors associated with the geographic distribution of leucocytozoa
parasitizing nestling eagle owls (Bubo bubo): a local spatial-scale analysis. Conservation
Genetics, 11, 1479-1487.
Patz, J.A., Graczyk, T.K., Geller, N. & Vittor, A.Y. (2000) Effects of environmental change on emerging
parasitic diseases. International Journal for Parasitology, 30, 1395-1405.
Phillips, K.P. (2009) Prevalence patterns and genetic characterisation of avian malaria in Tenerife: a
multi-species approach. Undergraduate, University of East Anglia.
R Development Core Team (2011) R: A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria.
Richardson, D.S., Jury, F.L., Blaakmeer, K., Komdeur, J. & Burke, T. (2001) Parentage assignment and
extra-group paternity in a cooperative breeder: the Seychelles warbler (Acrocephalus
sechellensis). Molecular Ecology, 10, 2263-2273.
Ricklefs, R.E., Fallon, S.M. & Bermingham, E. (2004) Evolutionary relationships, cospeciation, and host
switching in avian malaria parasites. Systematic Biology, 53, 111-119.
Ron, S.R. (2005) Predicting the distribution of the amphibian pathogen Batrachochytrium
dendrobatidis in the New World. Biotropica, 37, 209-221.
Saliternik, Z. (1955) The specific biological characteristics of Anopheles sergentii and their correlation
with malaria control in Israel. Bulletin of Enthomological Research, 46, 446-452.
Salje, H., Lessler, J., Endy, T.P., Curriero, F.C., Gibbons, R.V., Nisalak, A., Nimmannitya, S., Kalayanarooj,
S., Jarman, R.G., Thomas, S.J., Burke, D.S. & Cummings, D.A.T. (2012) Revealing the microscale
23
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
spatial signature of dengue transmission and immunity in an urban population. Proceedings
of the National Academy of Sciences of the United States of America, 109, 9535-9538.
Sehgal, R.N.M., Buermann, W., Harrigan, R.J., Bonneaud, C., Loiseau, C., Chasar, A., Sepil, I., Valkiunas,
G., Iezhova, T., Saatchi, S. & Smith, T.B. (2011) Spatially explicit predictions of blood parasites
in a widely distributed African rainforest bird. Proceedings of the Royal Society B-Biological
Sciences, 278, 1025-1033.
Serandour, J., Girel, J., Boyer, S., Ravanel, P., Lemperiere, G. & Raveton, M. (2007) How human
practices have affected vector-borne diseases in the past: a study of malaria transmission in
Alpine valleys. Malaria Journal, 6.
Sleeman, J.M., Howell, J.E., Knox, W.M. & Stenger, P.J. (2009) Incidence of Hemorrhagic Disease in
White-Tailed Deer Is Associated with Winter and Summer Climatic Conditions. Ecohealth, 6,
11-15.
Spurgin, L.G., Illera, J.C., Padilla, D.P. & Richardson, D.S. (2012) Biogeographical patterns and cooccurrence of pathogenic infection across island populations of Berthelot's pipit (Anthus
berthelotii). Oecologia, 168, 691-701.
Valkiunas, G. (2005) Avian malaria parasites and other haemosporidia. CRC Press, New York.
Van Riper, C., III, Van Riper, S.G., Goff, M.L. & Laird, M. (1986) The epizootiology and ecological
significance of malaria in hawaiian usa land birds. Ecological Monographs, 56, 327-344.
Waldenstrom, J., Bensch, S., Hasselquist, D. & Ostman, O. (2004) A new nested polymerase chain
reaction method very efficient in detecting Plasmodium and Haemoproteus infections from
avian blood. Journal of Parasitology, 90, 191-194.
Wood, M.J., Cosgrove, C.L., Wilkin, T.A., Knowles, S.C.L., Day, K.P. & Sheldon, B.C. (2007) Withinpopulation variation in prevalence and lineage distribution of avian malaria in blue tits,
Cyanistes caeruleus. Molecular Ecology, 16, 3263-3273.
Xiao, D., Long, Y., Wang, S., Fang, L., Xu, D., Wang, G., Li, L., Cao, W. & Yan, Y. (2010) Spatiotemporal
distribution of malaria and the association between its epidemic and climate factors in Hainan,
China. Malaria Journal, 9.
Yotoko, K.S.C. & Elisei, C. (2006) Malaria parasites (Apicomplexa, Haematozoea) and their
relationships with their hosts: is there an evolutionary cost for the specialization? Journal of
Zoological Systematics and Evolutionary Research, 44, 265-273.
Zhou, S.S., Huang, F., Wang, J.J., Zhang, S.S., Su, Y.P. & Tang, L.H. (2010) Geographical, meteorological
and vectorial factors related to malaria re-emergence in Huang-Huai River of central China.
Malaria Journal, 9.
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672
673
674
675
676
677
678
679
680
681
682
683
684
24
685
Figure legends
686
687
Figure 1. Map of Tenerife…
688
689
Figure 2. Plots of predicted probabilities derived from the logistic regression models of a) distance to
690
water reservoirs and b) distance to poultry farms with LK6 infection status as the response variable.
691
Histograms show frequency of healthy (lower end)and infected (upper end) individuals at each 100 m
692
distance class.
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
25
710
Table 1. Summary of single-predictor generalised linear (non-spatial) and autologistic (spatial) models
711
for the effect of environmental variables on the presence of malaria in Berthelot’s pipits (fitted as a
712
binary response variable)). AIC, p-value, Nagelkerke R2 and rank order of predictors based on AIC are
713
shown.
Non-spatial models
Predictor
ALT
PRECIP
MINTEMP
ASPECT
SLOPE
DISTWATER
DISTPOUL
DISTFARM
DIST_URB
DENSITY
VEGTYPE
Spatial models
Coefficient
R2
p-value
AIC
Rank
Coefficient
R2
p-value
AIC
Rank
-0.001
0.057
<0.001
493.8
4
-0.001
0.118
0.005
477.3
4
-0.005
0.052
<0.001
495.2
5
-0.004
0.109
0.006
480.1
6
0.201
0.077
<0.001
487.6
2
0.167
0.129
0.001
473.9
2
-0.002
0.009
0.104
507.6
9
-0.002
0.088
0.244
486.6
9
-0.034
0.006
0.194
508.5
10
-0.019
0.085
0.480
487.4
11
-0.001
0.080
<0.001
486.9
1
-0.001
0.139
0.002
470.6
1
-1.4e-4
0.073
<0.001
489.1
3
-0.001
0.123
0.001
475.8
3
-0.3e-4
0.026
0.002
495.4
6
-3.1e-4
0.118
0.008
477.3
5
-0.001
0.026
0.028
502.9
7
-0.001
0.099
0.063
482.9
7
1.150
0.020
0.018
504.6
8
0.688
0.089
0.179
486.1
8
15.00
0.022
1.000
509.8
11
15.00
0.106
1.000
486.9
10
714
715
716
717
718
719
720
721
722
26
723
Table 2. Autologistic models with AIC ≤ 2 when compared to the best fit model out of the 511
724
possible models generated.
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
Model rank
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
725
27
726
Table 3. Summed Akaike weights (wi) from all possible models that contain each predictor out of the
727
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
728
28
729
Table 4. Summary of best-fit multi-predictor autologistic models for the effect of each of four
730
environmental variable categories (biotic, abiotic, natural, and anthropogenic) on the presence of
731
malaria in Berthelot’s pipits. These categories of effect were assessed by comparing all possible
732
models containing variables for each category in turn. Coefficients and p-values of predictors, as well
733
as the AIC and Nagelkerke R2 of the best fit model for each category are shown.
734
Category
Anthropogenic
Natural
Predictor
Anthropogenic
Natural
Coefficient
p-value
DISTWATER
-7.60E-04
DISTFARM
-
DIST_URB
DISTPOUL
Coefficient
Biotic
p-value
Coefficient
Abiotic
Coefficient
p-value
0.010
-0.001
0.037
-
-
-
-
-
-
-
-7.75E-05
0.020
-1.20E-04
p-value
<0.001
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
735
736
737
738
739
740
741
29
742
Supplementary table 1. Summary of single and multi-predictor autologistic models for the 50 m and
743
200 m radii for the sensitivity analyses.
Model
Altitude
Precipitation
Mintemp
Aspect
Slope
Water
Poultry
Farm
Density
Vegetation
Construction
Best fit anthropogenic
Best fit natural
Best fit biotic
Best fit abiotic
Predictors
ALT
PRECIP
MINTEMP
ASPECT
SLOPE
DISTWATER
DISTPOUL
DISTFARM
DENSITY
VEGTYPE
DIST_URB
DISTWATER
DISTPOUL
MINTEMP
ASPECT
DISTPOUL
ASPECT
DISTWATER
50m radius
AIC
pval
477.3
0.005
482.1
0.001
473.8
0.001
481.1
0.010
487.4
0.447
470.5
0.002
475.8
0.001
477.3
0.008
486.2
0.181
486.0
0.982
482.9
0.063
0.010
466.9
0.020
0.001
469.0
0.009
475.8
0.001
0.004
464.2
0.002
R2
0.118
0.102
0.129
0.106
0.085
0.139
0.123
0.118
0.089
0.109
0.100
0.157
0.151
0.123
0.167
Predictors
ALT
PRECIP
MINTEMP
ASPECT
SLOPE
DISTWATER
DISTPOUL
DISTFARM
DENSITY
VEGTYPE
DIST_URB
DISTWATER
DISTPOUL
MINTEMP
ASPECT
DISTPOUL
ASPECT
DISTWATER
200m radius
AIC
477.3
482.6
474.0
480.6
487.2
470.6
475.8
477.3
487.0
491.9
482.9
466.9
469.4
475.8
464.1
pval
0.005
0.023
0.001
0.008
0.387
0.002
0.001
0.008
0.320
1
0.063
0.010
0.020
0.002
0.011
0.001
0.004
0.002
R2
0.118
0.101
0.129
0.107
0.085
0.139
0.123
0.118
0.087
0.097
0.099
0.157
0.150
0.123
0.166
744
30