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Identifying barriers that govern parasite community assembly and parasite invasion risk is critical to understand how shifting host ranges impact disease emergence. We studied regional variation in the phylogenetic compositions of bird species and their blood parasites (Plasmodium and Haemoproteus spp.) to identify barriers that shape parasite community assembly. Australasia and Oceania. We used a data set of parasite infections from >10,000 host individuals sampled across 29 bioregions. Hierarchical models and matrix regressions were used to assess the relative influences of interspecies (host community connectivity and local phylogenetic distinctiveness), climate and geographic barriers on parasite local distinctiveness and composition. Parasites were more locally distinct (co-occurred with distantly related parasites) when infecting locally distinct hosts, but less distinct (co-occurred with closely related parasites) in areas with increased host diversity and community connectivity (a proxy for parasite dispersal potential). Turnover and the phylogenetic symmetry of parasite communities were jointly driven by host turnover, climate similarity and geographic distance. Interspecies barriers linked to host phylogeny and dispersal shape parasite assembly, perhaps by limiting parasite establishment or local diversification. Infecting hosts that co-occur with few related species decreases a parasite's likelihood of encountering related competitors, perhaps increasing invasion potential but decreasing diversification opportunity. While climate partially constrains parasite distributions, future host range expansions that spread distinct parasites and diminish barriers to host shifting will likely be key drivers of parasite invasions.
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Diversity and Distributions. 2017;1–11. wileyonlinelibrary.com/journal/ddi  
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© 2017 John Wiley & Sons Ltd
DOI: 10.1111/ddi.12661
BIODIVERSITY RESEARCH
Climate, host phylogeny and the connectivity of host
communities govern regional parasite assembly
Nicholas J. Clark1| Sonya M. Clegg2| Katerina Sam3| William Goulding4,5|
Bonny Koane6| Konstans Wells7
1School of Veterinary Science, University of
Queensland, Gatton, Qld, Australia
2Edward Grey Institute of Field
Ornithology, Department of
Zoology, University of Oxford, Oxford, UK
3Biology Centre CAS, Faculty of
Science, Institute of Entomology and
University of South Bohemia, Branisovska,
Ceske Budejovice, Czech Republic
4The Landscape Ecology and Conservation
Group, School of Earth and Environmental
Science, University of Queensland, St Lucia,
Qld, Australia
5Biodiversity and Geosciences
Program, Queensland Museum, South
Brisbane, Qld, Australia
6The New Guinea Binatang Research Centre,
Madang, Papua New Guinea
7Environmental Futures Research
Institute, School of Environment, Griffith
University, Nathan, Qld, Australia
Correspondence
Nicholas J. Clark, School of Veterinary Science,
University of Queensland, Gatton, Qld,
Australia.
Email: nicholas.j.clark1214@gmail.com
Funding information
Griffith University New Researcher Grant;
National Geographic Society Committee for
Research and Exploration Grant, Grant/Award
Number: 9383-13; Birds Queensland; Czech
Science Foundation, Grant/Award Number:
14-36098G; staff of The New Guinea
Binatang Research Centre; Darwin Initiative
for the Survival of Species, Grant/Award
Number: 22-00
Editor: Jeremy Austin
Abstract
Aim: Identifying barriers that govern parasite community assembly and parasite inva-
sion risk is critical to understand how shifting host ranges impact disease emergence.
We studied regional variation in the phylogenetic compositions of bird species and
their blood parasites (Plasmodium and Haemoproteus spp.) to identify barriers that
shape parasite community assembly.
Location: Australasia and Oceania.
Methods: We used a data set of parasite infections from >10,000 host individuals
sampled across 29 bioregions. Hierarchical models and matrix regressions were used
to assess the relative influences of interspecies (host community connectivity and
local phylogenetic distinctiveness), climate and geographic barriers on parasite local
distinctiveness and composition.
Results: Parasites were more locally distinct (co- occurred with distantly related para-
sites) when infecting locally distinct hosts, but less distinct (co- occurred with closely
related parasites) in areas with increased host diversity and community connectivity (a
proxy for parasite dispersal potential). Turnover and the phylogenetic symmetry of
parasite communities were jointly driven by host turnover, climate similarity and geo-
graphic distance.
Main conclusions: Interspecies barriers linked to host phylogeny and dispersal shape
parasite assembly, perhaps by limiting parasite establishment or local diversification.
Infecting hosts that co- occur with few related species decreases a parasite’s likelihood
of encountering related competitors, perhaps increasing invasion potential but de-
creasing diversification opportunity. While climate partially constrains parasite distri-
butions, future host range expansions that spread distinct parasites and diminish
barriers to host shifting will likely be key drivers of parasite invasions.
KEYWORDS
community assembly, host shifting, host specificity, interspecies barriers, parasite invasion,
Plasmodium
1 | INTRODUCTION
Regional variation in community composition is a central property in
nature (Kraft, Cornwell, Webb, & Ackerly, 2007; Wallace, 1876). With
increasing environmental destabilization and biotic homogenization,
predicting how ecosystems will function following disturbance relies
on identifying processes that govern community assembly (Ricklefs,
1987; Barnagaud et al., 2014; see Table 1 for bold term definitions).
Understanding parasite community assembly is crucial, as changes to
parasite composition or the frequency of host–parasite interactions
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can alter risks of parasite invasions and emerging disease (Adlard,
Miller, & Smit, 2015; Agosta, Janz, & Brooks, 2010; Brooks & Hoberg,
2007; Hoberg & Brooks, 2008; Lafferty, 2009).
A strong incentive exists to identify barriers to species estab-
lishment and determine how these barriers modulate invasion risk
(Hoberg, 2010; Kelly, Paterson, Townsend, Poulin, & Tompkins, 2009;
Springborn et al., 2015). For parasites, geographic barriers (such as
distance or mountain ranges) are known to constrain species’ distri-
butions (Brooks & Ferrao, 2005; Krasnov, Shenbrot, Khokhlova, &
Degen, 2016; Lafferty, 2009; Warburton, Kohler, & Vonhof, 2016).
In addition, environmental barriers (such as temperature and precip-
itation) drive development or transmission rates for many parasites,
especially vector- borne parasites such as those causing malaria and
lyme disease (Epstein, 2001; Githeko, Lindsay, Confalonieri, & Patz,
2000; Patz, Campbell- Lendrum, Holloway, & Foley, 2005). However,
parasite distributions are also linked to host life histories and distribu-
tions (Poulin, Krasnov, & Mouillot, 2011; Olsson- Pons, Clark, Ishtiaq,
& Clegg, 2015; Fecchio et al., 2017). Such interspecies barriers are
increasingly recognized to govern local assembly (HilleRisLambers,
Adler, Harpole, Levine, & Mayfield, 2012; Mayfield & Stouffer, 2017;
Wisz et al., 2013). Predicting how parasite composition may change in
the future relies on defining a consistent framework to identify pat-
terns that improve knowledge of assembly and elucidate underlying
mechanisms acting as barriers. Such patterns may be driven by a hier-
archical process, where parasites must first break through geographic
and/or environmental barriers to initially colonize a new range (Agosta
et al., 2010; Brooks & Hoberg, 2007). Following colonization, assem-
bly may be limited by interspecies barriers that govern parasite spread
and diversification (Figure 1). This process, termed “ecological fitting”
(Janzen, 1985), suggests many parasites are capable of infecting a
broader range of hosts than is currently realized, with changes to host
and/or parasite distributions producing new associations that may be
limited by host phylogenetic relationships (Araujo et al., 2015; Brooks
& Ferrao, 2005; Radtke, McLennan, & Brooks, 2002).
For parasites that rely on host dispersal to colonize new areas, re-
gions comprising a diversity of host species whose ranges overlap with
other potential hosts (i.e., high distributional connectivity to other re-
gions; “host community connectivity”) should support broader parasite
diversity due to increased niche space (Hector, Dobson, Minns, Bazeley-
White, & Hartley Lawton, 2001, Viana, D. S., Santamaría, L., & Figuerola,
J. (2016)) and a higher likelihood for parasites to break geographic and/
or environmental barriers (Figure 1). However, biotic barriers could still
limit parasite invasions in phylogenetically diverse systems, particularly
if invasion success is positively related to the invader’s local phyloge-
netic distinctiveness (i.e., more locally distinct invaders are less likely
to be limited by related competitors; HilleRisLambers et al., 2012). Yet,
while host community connectivity can overcome geographic dispersal
barriers, few studies recognize this aspect as a potential driver of para-
site assembly (but see Buckee, Danon, & Gupta, 2007).
Parasites are often restricted to hosts with phylogenetically con-
served ecological or physiological traits (Janzen, 1968; Rohde, 1980;
Schulze- Lefert & Panstruga, 2011; Streicker et al., 2010), a phenome-
non that has powerful consequences for species interactions and eco-
system functioning (Ehrlich & Raven, 1964; Hoberg & Brooks, 2008).
As parasites with high host specificity may be unable to shift hosts,
the local availability of suitable hosts can present an invasion barrier
following initial dispersal, especially if parasites are adapted to hosts
that do not commonly co- occur with closely related species (Brooks,
1979; Ewen et al., 2012; Clark & Clegg, 2015; Ellis et al., 2015; Mata,
da Silva, Lopes, & Drovetski, 2015; Figure 1).
While ecological fitting (governed at least partly by parasite
host-specificity and host evolutionary history) and host dispersal po-
tential are clearly important mechanisms impacting parasite establish-
ment and diversification, identifying their roles in natural host–parasite
systems is challenging. We develop a framework to identify relative
influences of barriers to regional parasite community assembly and
apply this framework to naturally occurring parasite infections from
Australasian bird communities. Haemosporidians (genera Plasmodium
TABLE1 Glossary of definitions for proposed community
assembly barriers and metrics used in analyses
Community assembly: The establishment and maintenance of local
communities through arrival of potential colonists from external
species pools.
Environmental barriers: Environmental differences between regions
that may govern species’ distributions, including variation in
macroclimate, habitat and altitude.
Geographic barriers: Physical barriers to between- region parasite
dispersal, including geographic distance, mountain ranges and water
barriers.
Host community connectivity: The distributional overlap of host
communities among regions, taking into account host species
richness and host geographic range sizes. Here, Sampled.ConH
describes host community connectivity while considering only
sampled avian host species, and Total.ConH describes connectivity
for all occurring avian species within a local assemblage.
Host specificity: the range and diversity of host species observed to
be infected by a parasite. Here, d’ describes parasite host-specificity
using host–parasite interaction networks, while STD* describes
phylogenetic host specificity using host phylogenetic distances.
Interspecies barriers: for parasites, interspecies barriers relate to
variation in host species attributes that prevent parasite spread and
diversification. These may include host phylogenetic relatedness and
ecological similarity (e.g., microhabitat use, nesting behaviour and
feeding behaviour).
Local phylogenetic distinctiveness: the average pairwise phylogenetic
distance between a focal taxon and co- occurring taxa within a local
assemblage. Here, DisP describes parasite species distinctiveness,
Sampled.DisH describes host species distinctiveness with respect to
co- occurring sampled host species, and Total.DisH describes host
species distinctiveness with respect to all co- occurring sampled avian
species.
Phylogenetic community skewness: a measure of the asymmetry of
species’ pairwise phylogenetic distances, where a left skew indicates
relatively more distantly than closely related species in a community,
while a right skew indicates the opposite.
Phylogenetic turnover (β): shifts in phylogenetic diversity between
communities. Here, βP describes parasite phylogenetic turnover,
Sampled.βH describes turnover of sampled host assemblages, and
Total.βH describes turnover of total avian assemblages.
    
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CLARK et AL.
and Haemoproteus) are vector- borne blood parasites that display a
rangeofhostspecificities(Križanauskienėetal.,2006).Duetolimited
vector dispersal (Ejiri et al., 2011), avian hosts are the primary vehi-
cles by which these parasites disperse (Pérez- Tris & Bensch, 2005).
Avian haemosporidians have been introduced to numerous bioregions,
sometimes with devastating effects on native birds, raising questions
about how interspecies and geographic barriers regulate parasite as-
sembly and invasion potential (Hellgren et al., 2014; van Riper, van
Riper, Goff, & Laird, 1986, Clark, Clegg & Lima 2014).
We assess barriers that may govern parasite local coexistence at
the species level by estimating effects of host community connectivity
and interspecies barriers (host phylogeny and parasite host-specificity)
on parasite local phylogenetic distinctiveness. We then address bar-
riers at the community level by (1) exploring effects of host phyloge-
netic turnover, environmental variation and geographic distance on
parasite turnover, and (2) testing if host connectivity or environmental
variation influence parasite phylogenetic community skewness. We
expect that increased host community connectivity reduces barriers
to parasite establishment, leading to phylogenetically homogenized
parasite communities. If host phylogeny acts as a relatively strong in-
terspecies barrier to parasite assembly, we expect that distinct hosts
carry distinct parasites and that between- region host turnover pre-
dicts parasite turnover. We also expect host- specialist parasites to
be more locally distinct than generalists, as specialists may have less
opportunity to diversify through host range expansions. Alternatively,
if higher diversities of host specialists are able to co- occur through
extensive niche packing (Ricklefs, 2010), then we expect specialists to
be less distinct than generalists.
2 | METHODS
2.1 | Host–parasite occurrence data and avian
community connectivity
We surveyed published literature and queried the MalAvi database
(http://mbio-serv2.mbioekol.lu.se/Malavi/; accessed September
2016; Bensch, Hellgren, & Pérez- Tris, 2009) to compile data from
>10,000 sampled host individuals (from 297 avian species) across
83 sites, ranging across latitudes −50.77 to 14.27 and longitudes
−159.78to178.07(Figure2).Inallcases,parasitelineageswereiden-
tified using PCR targeting the cytochrome- b (cyt- b) gene (Hellgren,
Waldenström, & Bensch, 2004; Waldenström, Bensch, Hasselquist,
& Östman, 2004). Evidence indicates lineages differing by as little
as one base pair may be reproductively isolated (Bensch, Pérez- Tris,
Waldenström, & Hellgren, 2004). We thus regard each unique se-
quence as a parasite “species”. Low numbers of recovered parasites
at some sites meant we could not assess within- site composition. We
thus grouped sites into 29 regions. Australian mainland sites were
grouped by climate zone using the Bureau of Meteorology’s Köppen
classification, which defines zones using temperature, precipitation
and vegetation data (http://www.bom.gov.au/jsp/ncc/climate_aver-
ages/climate-classifications/; accessed November 2016). Papua New
Guinea mainland sites were grouped based on elevation (highlands,
mean altitude = 2,500 m; and lowlands, mean altitude = 60 m). Island
sites were either grouped by island (if at least three parasite species
were recovered) or into regions representing nearby islands in an ar-
chipelago (Figure 2; Data set S1).
FIGURE1 Schematic illustrating potential barriers to regional spread and diversification for parasites that rely on host movement for
dispersal. Plates represent different bioregions, while zones (forest, mountain) within plates represent different habitat types. At the bottom
left is a sectional zoom of the forested habitat in the left- hand plate, illustrating within- region parasite diversification where closely related
host species enable the breakdown of interspecies barriers. Shown in black is the focal host of a given parasite species, with ecologically or
phylogenetically similar host species depicted as similar shapes in varying shades of grey. A distantly related host species is depicted as a
different body shape. Concentric oval shapes represent parasites, with different shapes and colours representing different parasite species
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We downloaded range maps for all avian species occurring in
the study area (N = 3,024 species) from BirdLife International and
NatureServe (http://www.birdlife.org/datazone; accessed October
2016). For each region, we obtained lists of occurring avian species (de-
fined as the “total” assemblage) by recording all species whose ranges
overlapped 111 km buffers (1° at the equator) around sites. Bird range
sizes were calculated as the total area of range polygons. Range sizes var-
ied from 1 km2 (island endemics) to 28,000 km2 (wide- ranging seabirds).
Avian community connectivity was calculated as an inverse
Simpson diversity index (Simpson 1949) using species’ range sizes as
weights (instead of using species abundances). Here, increased species
richness, larger species range sizes and more even range size distri-
butions all lead to increased collective mobility of a local host assem-
blage. Two connectivity indices were created, one using sampled hosts
(Sampled.ConH) and second using total assemblages (all occurring avian
species; Total.ConH). We included Total.ConH because many haemospo-
ridians infect a diversity of avian species (Ewen et al., 2012; Olsson-
Pons et al., 2015), suggesting unsampled but present host species
impact parasite assembly. This will be especially relevant for generalist
parasites, whereas sampled hosts should be representative for spe-
cialized parasites that are unlikely to occur in unsampled host species.
2.2 | Parasite and host phylogenetic reconstructions
Parasite cyt- b sequences (205 Haemoproteus and 80 Plasmodium par-
asites) were used to reconstruct phylogenetic relationships in beast
v1.8.1 (Drummond & Rambaut, 2007; See Fig. S1). We identified the
best evolutionary model (HKR+G) using maximum likelihood in mega
v7.0 (Tamura, Dudley, Nei, & Kumar, 2007). We specified a Yule spe-
ciation prior and ran two chains of 17,500,000 iterations, sampling
every 100,000 and removing 2,500,000 samples as burn- in. Chains
were examined visually for stationarity and convergence.
Avian phylogenies were gathered from Birdtree.org (http://
birdtree.org; accessed September 2016), which contains a Bayesian
posterior distribution of phylogenies for 9,993 avian species (Jetz,
Thomas, Joy, Hartmann, & Mooers, 2012). We gathered 100 trees
from the “Ericsson All Species Trees” data set for the 297 sampled
host species, and another 100 trees for the 3,024 avian species oc-
curring in the sample area. For all trees, branch lengths represented
substitutions per site and were scaled (dividing branch lengths by the
maximum) prior to analyses.
2.3 | Species- level analyses
2.3.1 | Host and parasite phylogenetic
distinctiveness
For sampled host species, local phylogenetic distinctiveness (Sampled.
DisH) was calculated as mean pairwise phylogenetic distance between
a focal species and all other sampled host species in a region. This dis-
tance was divided by the mean of all pairwise distances in the region,
resulting in region- specific distinctiveness (higher values indicating
FIGURE2 Distribution of parasites across the study area. Lines connect phylogenetic parasite lineages to the region where they were most
frequently observed. Circle sizes are inversely proportional to mean phylogenetic turnover (βP) between the region and remaining regions,
accounting for geographic distance. Hence, larger circles show communities with lower mean turnover to surrounding regions, which can be
thought of as having more “connected” parasite communities. Lines and circles are coloured according to region, with closely situated regions
grouped to improve clarity
    
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CLARK et AL.
more distinct species). We calculated total host distinctiveness (Total.
DisH) using mean phylogenetic distance between a sampled host
and all occurring avian species (sampled and unsampled) in a region.
Parasite distinctiveness (DisP) was calculated separately for each para-
site genus.
2.3.2 | Parasite host-specificity
Two indices described parasite host-specificity. First, we built bipar-
tite networks (using numbers of infected individuals for each host spe-
cies) and calculated the d’ specialization index using Kullback–Leibler
distances (Blüthgen, Menzel, & Blüthgen, 2006). Ranging from zero
(no specialization; i.e., using all available hosts) to one (perfect special-
ist), d’ quantifies how strongly a parasite is “specialized” compared to
other parasites in terms of host range and interaction frequencies. We
calculated phylospecificity for each parasite (STD*; Poulin & Mouillot,
2005), which accounts for the number of infected host species and
their phylogenetic distances. Because STD* ranges from one (special-
ist) to greater than one, we used inverse STD* so both metrics could
be interpreted in the same scale and direction. Parasite STD* and d’
were uncorrelated (Pearson correlation; t = −1.41,p = .16), suggesting
they capture different aspects of parasite host-specificity (d’ capturing
the level of host sharing by parasites and STD* capturing phylogenetic
relationships of infected hosts).
2.3.3 | Influences of host community connectivity,
host phylogeny and host specificity on parasite
distinctiveness
We tested whether interspecies barriers influenced parasite distinc-
tiveness (DisP) with a hierarchical linear model, using 548 unique
parasite*host*region combinations as data points (Data set S2).
Because DisP indices were non- negative and positively skewed, we
log- transformed values and specified a Gaussian error distribu-
tion. Continuous predictors were the two host distinctiveness met-
rics (Sampled.DisH, Total.DisH), the two host connectivity metrics
(Sampled.ConH, Total.ConH), host geographic range and both parasite
host-specificity metrics (d’, STD*). Because parasite genera showed
different phylogenetic patterns (see Results) and Total.DisH explained
a significant proportion of variance in DisP in preliminary analyses, we
tested a Total.DisH*parasite genus interaction. To decompose varia-
tion among covariates and account for underlying phylogeographic
structure, host phylogeny and sample region were included as random
grouping terms, allowing inferences for group- specific slopes whilst
estimating between- group variation (Gelman & Hill, 2007).
The model was fitted in a Bayesian framework using R package mc-
mcglmm (Hadfield, 2010). We used a flat prior for residual variance and
parameter expansion (redundant multiplicative reparameterization
of the linear model) for grouping terms, which reduces dependence
among parameters and improves mixing (Gelman, 2006). To account
for phylogenetic uncertainty, we ran separate models across 50 host
trees (Guillerme & Healy, 2014). Models were run using two chains
of 100,000 iterations with burn- in of 10,000 and thinning interval
of 300. Chains were inspected for mixing/convergence both visu-
ally and with the Gelman–Rubin diagnostic (Gelman & Rubin, 1992).
Autocorrelations were calculated to ensure independence of coeffi-
cient estimates (all autocorrelations <0.1).
2.4 | Community analyses
2.4.1 | Interspecies and geographic barriers to
parasite phylogenetic turnover
To describe shifts in diversity among regions, parasite phylogenetic
turnover (βP) was calculated (using binary occurrence data; Tsirogiannis
& Sandel, 2015) between regions where three or more parasites oc-
curred. Host turnover was calculated using either sampled hosts
(Sampled.βH) or total avian assemblages (Total.βH). Distances between
paired regions were calculated as beeline distance (km) between cen-
tral points (mean latitude and longitude of regions). Regional climate
dissimilarity was captured by three Gower’s distance matrices (Gower,
1971) to describe temperature and precipitation variation (both of
which are thought to influence haemosporidian distributions; Sehgal
et al., 2010; Sehgal, 2015). We used minimum temperature of the
coldest month and mean temperature of the coldest quarter in a min.
temp matrix, while a max.temp matrix included maximum temperature
of the warmest month and mean temperature of the warmest quarter.
Mean yearly precipitation and precipitations of the wettest and driest
quarters were included in a precip matrix. For climate matrices, vari-
ables were sourced from (www.worldclim.org; accessed November
2016) and were continuous, unweighted and scaled by range (dividing
by the maximum).
We tested if βP was correlated with Sampled.βH, Total.βH, geographic
distance or climate dissimilarity matrices using multiple regressions on
distance matrices (MRM; Goslee & Urban, 2007). Phylogenetic uncer-
tainty was captured by repeating regressions over 1,000 iterations,
where β values were re- calculated in each iteration using randomly
sampled (with replacement) trees. To account for sampling variation
that could bias turnover estimates (rare species may be more likely to
be observed with larger sample sizes), we randomly removed subsets
of species from well- sampled regions (>8 observed parasite species)
prior to regression. We arbitrarily allowed the proportion of removed
species to vary across a uniform distribution from zero to 30% in each
iteration. Regression coefficients and R2 values were gathered from
the 1,000 iterations.
2.4.2 | Barriers to parasite phylogenetic
community skewness
Host and parasite phylogenetic community skewness were calculated
using pairwise phylogenetic distance distributions. A measure of sym-
metry, this index will be less than zero (right- skewed) if communities
are made up of relatively more closely than distantly related species
(Schweiger, Klotz, Durka, & Kühn, 2008), suggesting future coloniz-
ing parasites have a greater likelihood of being locally distinct. Thus,
regions with right- skewed communities may be more vulnerable to
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invasions by distantly related species if parasites are able to overcome
environmental barriers and colonize. Skewness was calculated for re-
gions where three or more parasites occurred.
We tested if parasite skewness was predicted by host connectiv-
ity (Sampled.ConH, Total.ConH) using linear regression with Gaussian
error distribution. Mean annual precipitation and mean temperatures
of the warmest and coldest quarters were included as continuous co-
variates to account for possible climate influences, while sampled and
total host skewness were included to account for influences of host
phylogenetic symmetry. Parasite genus was included as a categorical
covariate. The model was fitted using mcmcglmm with a flat prior for re-
sidual variance. We ran two chains of 100,000 iterations with burn- in
of 10,000 and thinning interval of 300, following procedures above to
examine convergence and estimate autocorrelations.
For all phylogenetic metrics (skewness, distinctiveness and STD*),
we accounted for phylogenetic uncertainty by calculating median
indices across 1,000 randomly sampled host and parasite trees.
Significance of model effects was determined by examining if 95%
quantiles (for MRM models) or 95% credible intervals (CI; for Bayesian
models) of regression coefficients did not overlap zero. Continuous
predictors were scaled (centred and divided by one standard devia-
tion), and variances explained were calculated following Nakagawa
and Schielzeth (2013). Data were analysed in R v3.2.1 (R Core Team,
2016; R: A language and environment for statistical computing). Data
and R code are presented in Supplementary Data and the Dryad Digital
Repository: (https://doi.org/10.6084/m9.figshare.5432107).
3 | RESULTS
3.1 | Host phylogeny, local distinctiveness and
connectivity drive parasite distinctiveness
Parasite distinctiveness (DisP) was strongly related to host phylogeny
(variance explained = 46.8%–78.3%), with hosts from certain clades
more likely to carry distinct parasites (Figure 3). These included car-
riers of distinct Haemoproteus spp. such as doves (Columbidae),
kingfishers (Alcedinidae) and corvoids such as crows (Corvidae) and
whistlers (Pachycephalidae; Figure 3), all of which occupy a range
of regions yet rarely co- occur with sympatric sister species (Dutson,
2012; Jønsson et al., 2014). After accounting for the strong influ-
ence of host phylogeny, DisP was also positively predicted by local
host total distinctiveness (Total.DisH; coefficient 95% CI = 0.04–0.12;
variance explained = 2.48%–6.38%; Figure 3), suggesting host relat-
edness to the local avian assemblage acts as an interspecies barrier to
parasite assembly. This relationship varied between parasite genera,
as increases in Total.DisH lead to a 1.95 times higher increase in DisP
for Haemoproteus than for Plasmodium parasites.
DisP decreased with increasing total host connectivity (Total.ConH;
coefficient = 0.01–0.09; variance explained = 0.04%–7.7%; See Fig.
S2), indicating greater host diversity and collective mobility increase
a parasite’s chance of encountering related parasites. Total.ConH was
highest in Malaysia (509 avian species; Total.ConH = 83.60) and south-
east Australia (468 avian species; Total.ConH = 80.42), moderate in
Papua New Guinea where many endemic avian species occur (mean
species = 520.5; mean Total.ConH = 42.62) and lowest in Vanuatu and
New Caledonia (mean species = 115 and 110; mean Total.ConH = 32.3
and 31.6, respectively). DisP was not influenced by Sampled.ConH,
Sampled.DisH or individual host range (coefficient CIs overlapped zero).
We observed considerable variation in host specificity for both
parasite genera, though neither specificity metric influenced DisP (co-
efficients overlapped with zero). For both genera, STD* (phylospeci-
ficity) ranged from 0.41 to 1 (mean = 0.79 and 0.87 for Plasmodium
and Haemoproteus, respectively), while d’ (network specificity) ranged
from 0 to 1 (means = 0.65 and 0.67). In total, fixed effects (d’, STD*,
host range size, Total.ConH, Sampled.ConH, Total.DisH, Sampled.DisH,) ex-
plained 5.7%–13.2% of variance in DisP while the full model (including
host phylogeny and region grouping terms) explained 69.8%–88.9%.
3.2 | Host phylogeny and climate shape parasite
community structure
We found evidence that both environmental and interspecies bar-
riers influence parasite turnover. For Plasmodium, βP was positively
correlated with Sample.βH (MRM coefficient = 1.01–1.86), indicating
host phylogeny influences shifts in parasite diversity. Plasmodium
βP also correlated positively with geographic distance (0.56–1.21),
but negatively with max.temp (−0.09 to −0.18). For Haemoproteus,
βP correlated positively with both host turnover metrics (Sampled.βH
coefficient = 0.30–0.61; Total.βH = 0.58–1.13), and with geographic
distance and max.temp (0.04–1.37; 0.16–0.45, respectively), but
FIGURE3 Distribution of local phylogenetic distinctiveness for
hosts (Total.DisH) and their parasites (DisP) across the host phylogeny.
Distinctiveness represents mean phylogenetic distance between the
focal species and all co- occurring species within a region. Values are
scaled so values >0 indicate taxa that are more distinct, while those
<0 indicate less distinct taxa
    
|
 7
CLARK et AL.
negatively with min.temp(−0.11to−0.28).Varianceexplainedbypre-
dictors ranged from 47% to 57% for Haemoproteus βP and from 4% to
11% for Plasmodium βP.
Mainland communities such as Papua New Guinea and eastern
Australia showed low mean parasite turnover among paired regions
(low average pairwise βP after accounting for geographic distance;
Figure 2; Data set S3), suggesting these assemblages were less phy-
logenetically unique within the study area. Parasite assemblages on
Melanesian islands (New Caledonia and Vanuatu) showed moderate
mean turnover, while relatively isolated and less well- sampled com-
munities such as Christmas Island and north- west Australia showed
high turnover (Figure 2). Plasmodium communities in New Zealand
and Micronesia, where many occurring parasites are known to be in-
troduced (Beadell et al., 2006; Ewen et al., 2012), showed high mean
turnover (Figure 2).
Parasite community skewness indices were predominantly nega-
tive (right- skewed; Figure 4), with assemblages generally made up of
more closely than distantly related parasites. Parasite skewness was
not influenced by host community connectivity or host skewness
but was driven by mean temperature of the coldest quarter (coeffi-
cient = 0.02–2.98; variance explained = 0.2%–10.6%), with colder
regions harbouring more negatively skewed communities (Figure 4).
Parasiteskewness also differedbetween genera(coefficient=−0.91
to −0.02; variance explained=7.5%–27.10%), with Plasmodium
more negatively skewed than Haemoproteus communities (Figure 4).
Interestingly, Haemoproteus communities in Papua New Guinea were
positively skewed, while those in eastern Australian were negatively
skewed (Figure 4), suggesting neighbouring parasite assemblages with
low phylogenetic turnover (Figure 2) can vary substantially in commu-
nity structure.
4 | DISCUSSION
We illustrate a framework for identifying relative influences of inter-
species, environmental and geographic barriers to parasite community
assembly. Using this framework, we show that host phylogeny is a key
driver of local parasite assembly, while climate and the regional con-
nectivity of host assemblages play lesser but nonetheless important
roles. Moreover, host phylogeny and geographic distance were more
important than environmental barriers in shaping parasite turnover,
indicating alterations to host movement and community composition
may strongly affect parasite dispersal and invasion potential across
biogeographic scales.
4.1 | Barriers to parasite community
assembly and their roles in parasite spread
Host phylogeny was an important driver of parasite distinctiveness
and species turnover, supporting suggestions that host identity drives
shifts in haemosporidian diversity and implicating host evolution-
ary history as a determinant of regional parasite assembly (Fecchio
et al., 2017; Scordato & Kardish, 2014). Phylogenetic signals are a
proxy for physical (i.e., physiological, morphological, biochemical) and
ecological traits, where closely related species resemble each other
more than random pairs, indicating conserved attributes likely play a
role in modulating interspecies barriers to regional parasite assembly
(Huang, Bininda- Emonds, Stephens, Gittleman, & Altizer, 2014). Yet,
an important consideration here is that we do not know which shared
host traits influence blood parasite assembly patterns. Determining
underlying interspecies barriers to parasite composition will require
additional interdisciplinary work, combining data on host traits with
FIGURE4 Parasite phylogenetic
community skewness across regions.
Skewness >0 indicates co- occurring
parasites are relatively distantly related
(left- skewed pairwise distance distribution),
while <0 indicates parasites are relatively
closely related (right- skewed distance
distribution). Regions are ordered based on
mean temperature of the coldest quarter,
with numbers in parentheses indicating
the number of parasites recovered in each
region. NZ, New Zealand; AUS, Australia;
NC, New Caledonia; VAN, Vanuatu; PNG,
Papua New Guinea
–2.5
−2.0
−1.5
−1.0
−0.5
0.0
0.5
1.0
NZ South Island (3
)
NZ North Island (7)
AUS south-east (14)
AUS central east (38)
NC Grande Terre (21)
AUS north-east (60)
NC Mare (5)
NC Lifou (8)
VAN southern islands (17)
NC Ouvea (7)
VAN west
ern islands (11)
AUS north-west (8)
VAN cent
ral islands (12)
VAN Espiritu Santo (8
)
PNG Highl
ands (18)
VAN northern islands (4)
PNG Lowlan
ds (42)
Philippines (58)
Malaysia (16)
Haemoproteus
Plasmodium
Warmer min tempColder min temp
More closely relatedLess closely related
8 
|
   CLARK et AL.
methods that can decompose phylogenetic and ecological similarity
to improve inference (Cadotte, Albert, & Walker, 2013; Clark & Clegg,
2017).
Future host range shifts may considerably impact parasite spread
and disease emergence, both by breaking down existing barriers to
host shifting and by increasing parasite dispersal (Atkinson & LaPointe,
2009; Young, Parker, Gilbert, Guerra, & Nunn, 2017). Here, a positive
relationship between host and parasite distinctiveness indicates that
diminishing phylogeographic barriers (where host range shifts may
alter local host distinctiveness) could present more opportunities for
parasites to shift between related hosts. Yet, a strong host phyloge-
netic signal, where distinct parasites are more strongly associated with
certain host clades, suggests alterations to host species’ distributions
may have different effects on parasite spread depending on host
evolutionary history. For instance, we identified multiple host clades
as prominent carriers of distinct parasites, including non- passerines
(kingfishers and doves) as well as certain passerine groups (crows and
whistlers), indicating that future range shifts for these host groups
could lead to novel parasite introductions. Our work therefore cor-
roborates a large body of literature to show that interactions between
ecological fitting and shifting geographic distributions will have pow-
erful influences on parasite assembly and emergence potential (Agosta
et al., 2010; Araujo et al., 2015; Brooks & Hoberg, 2007; Hoberg,
2010; Hoberg & Brooks, 2008). However, a significant influence of
host community connectivity suggests that parasite distinctiveness is
not only driven by host phylogeny, but also by forces that limit host
diversity and distributional overlap (i.e., competitive exclusion or dis-
persal barriers; Ricklefs, 2010; Ewen et al., 2012). This finding gener-
ates exciting new avenues for studying parasite assembly, particularly
as few studies relate the connectivity of host communities to parasite
dispersal opportunity (but see Buckee et al., 2007).
Our findings that environmental effects influence parasite turn-
over and community skewness agree with previous studies to suggest
that even if dispersal barriers break down, climate and perhaps other
environmental conditions may constrain parasite distributions (Clark,
Clegg, & Klaassen, 2016; Clark, Wells, Dimitrov, & Clegg, 2016; Kutz,
Hoberg, Molnár, Dobson, & Verocai, 2014; Sehgal, 2015). Indeed, re-
gional temperature similarity impacted shifts in diversity for both para-
site genera, albeit with different directional relationships. One possible
explanation could be that haemosporidians are subject to influences
of external temperature changes on ectothermic vectors (Paaijmans,
Imbahale, Thomas, & Takken, 2010), and Plasmodium and Haemoproteus
parasites are transmitted by different arthropods (mosquitoes from
family Culicidae and midges from family Ceratopogonidae, respec-
tively; Santiago- Alarcon, Palinauskas, & Schaefer, 2012). However,
little is known about the particular vector species transmitting avian
haemosporidians in the South Pacific (but see Ishtiaq et al., 2008),
and so, drawing conclusions from these different patterns remains
challenging. Intriguingly, regions with colder temperatures harboured
more closely related communities for both parasite genera, perhaps
indicating minimum temperatures act as a strong filter for haemospo-
ridian diversity, a finding that warrants future study. Regardless of the
biological mechanism, accounting for interspecies interactions and
environmental conditions can improve predictions of species distri-
butions following climate shifts (Choler, Michalet, & Callaway, 2001;
Mayfield & Stouffer, 2017; Wells, Feldhaar, & O’Hara, 2014).
Determining which species are likely to be introduced and be-
come invasive are prominent ecological questions (Springborn et al.,
2015; Wiens, 2011). Our results suggest that parasites introduced to
regions with low host community connectivity, high host turnover and
low minimum temperatures may be more likely to invade the com-
munity. These patterns highlight that New Zealand, which showed
high rates of host and parasite turnover and contained distantly re-
lated (phylogenetically left- skewed) Plasmodium communities, may
be particularly vulnerable to invasions. Distinct invaders can have key
competitive advantages and a greater chance of becoming invasive
(HilleRisLambers et al., 2012), as has been the case in the Galapágos
where the invasive fly, Philornis downsi, parasitizes a diversity of en-
demic bird species (Fessl, Sinclair, & Kleindorfer, 2006). Indeed, inva-
sive avian malaria parasites have already been recorded infecting a
diversity of native New Zealand birds, with evidence suggesting that
introduced birds play key roles in driving parasite spread (Ewen et al.,
2012; Schoener, Banda, Howe, Castro, & Alley, 2013). Parasites intro-
duced to highly connected host regions, on the other hand, may be
more likely to experience competition with closely related parasites,
perhaps curbing invasion potential. Under this consideration, areas
such as eastern Australia and mainland Papua New Guinea may be
less vulnerable to parasite invasions (though not immune; see Clark,
Olsson- Pons, Ishtiaq, & Clegg, 2015), as these regions contain a rela-
tively balanced phylogenetic diversity of parasites and experience high
host community connectivity.
4.2 | Accounting for unsampled host species in
parasite assembly studies
Our study raises a critical point for assessing parasite composition, as
measures of host relationships were more important in driving para-
site assembly when considering the total host assemblage rather than
only sampled hosts. A host’s distinctiveness with respect to the entire
avian community positively predicted parasite distinctiveness, while
considering only sampled hosts had no influence on parasite distinc-
tiveness. Phylogenetic turnover of the total avian assemblage was also
a stronger predictor of Haemoproteus turnover than was sampled host
turnover. These findings imply that variation in unsampled but locally
present host species is important for driving parasite establishment.
Inferences beyond those obtained from sampled hosts are clearly
needed, a process which is rarely considered in host–parasite interac-
tions (but see Wells et al., 2012), despite being a well- known problem
in the sample survey literature (Little, 2004).
4.3 | Caveats and conclusions
There are several ways in which our study framework can be im-
proved. First, we did not consider individual sites in our study as our
data were limited by small sample sizes for many sites. Inclusion of
site- specific species and climate data could be used as an additional
    
|
 9
CLARK et AL.
source of information to examine possible impacts of sampling bias on
regional community inferences. Second, consideration of sampling dis-
tribution across regions may have an impact on community turnover
estimates, as regions such as Christmas Island and Micronesia had a
relatively high turnover that could have been influenced by low overall
sample sizes and large geographic distances to many other study re-
gions. Future studies that sample smaller and more regular geographic
intervals could help to address this drawback. Finally, our phylogenetic
metrics relied only on binary species occurrences (present or absent)
and may be improved with better consideration of species’ relative
abundances, as host abundance plays a role in host reservoir poten-
tial and cross- species parasite transmission (Kilpatrick, Kramer, Jones,
Marra, & Daszak, 2006). Unfortunately, such data for host abundance
were not available and would require additional field survey efforts.
In summary, our study agrees with previous work to suggest that
in addition to identifying environmental barriers, considering host
phylogenetic relationships and dispersal abilities is key to understand-
ing regional parasite assembly (Agosta et al., 2010; Brooks & Ferrao,
2005; Sehgal, 2015; Wells, O’Hara, Morand, Lessard, & Ribas, 2015).
Moreover, we show that accounting for the overall connectivity of the
host community, rather than solely focussing on individual host spe-
cies’ dispersal potentials, may be crucial to predicting future parasite
invasions. With the pervasive need to understand how interspecies
interactions shape species distributions (Wisz et al., 2013), our study
represents an important step towards predicting how parasite assem-
blages will be shaped following future global change.
ACKNOWLEDGEMENTS
We thank S. Olsson- Pons, D. Treby, K. Lowe, J. LeBreton, S. Oghino, F.
Cugny, W. Waheoneme, T. Read, Association pour la Sauvegarde de la
Biodiversité d’Ouvéa and Société Calédonienne d’Ornithologie for field
and/or logistical assistance in Australia and New Caledonia. Australian
and Melanesian fieldwork was conducted under Griffith University
ethics clearance ENV/01/12/AEC (SC). Papua New Guinea fieldwork
was conducted under UQ animal ethics permit GPEM/172/13/APA
(WG). SC acknowledges financial support from a Griffith University
New Researcher Grant, a National Geographic Society Committee for
Research and Exploration Grant (#9383- 13) and Birds Queensland.
KS acknowledges financial support from Czech Science Foundation
14- 36098G and staff of The New Guinea Binatang Research Centre.
BK was funded by Darwin Initiative for the Survival of Species No.
22- 00. Work of KS and BK was conducted under University of South
Bohemia animal ethic clearance 515- 20424/2012- 30 and PNG NRI
Research Permit No. 99902077829. We thank two anonymous refer-
ees for comments that improved the manuscript.
DATA ACCESSIBILITY
Newly reported parasite sequences will be uploaded to GenBank
and the MalAvi avian malaria database upon acceptance. R code
and raw data sets are uploaded to figshare https://doi.org/10.6084/
m9.figshare.5432107
ORCID
Nicholas J. Clark http://orcid.org/0000-0001-7131-3301
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BIOSKETCH
Nicholas Clark is a disease ecologist interested in evolutionary
ecology and the biogeography of wildlife pathogens. His research
interests concern topics in computational phylogenetics, biodiver-
sity and host–pathogen interactions, as well as large- scale patterns
in community assembly. http://nicholasjclark.weebly.com/.
Author contributions: N.J.C. and K.W. conceived the ideas; all au-
thors contributed to data collection; N.J.C. and K.W. analysed the
data and led the writing.
SUPPORTING INFORMATION
Additional Supporting Information may be found online in the sup-
porting information tab for this article.
How to cite this article: Clark NJ, Clegg SM, Sam K, Goulding
W, Koane B, Wells K. Climate, host phylogeny and the
connectivity of host communities govern regional parasite
assembly. Divers Distrib. 2017;00:1–11. https://doi.
org/10.1111/ddi.12661
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Natural communities are well known to be maintained by many complex processes. Despite this, the practical aspects of studying them often require some simplification, such as the widespread assumption that direct, additive competition captures the important details about how interactions between species impact community diversity. More complex non-additive ‘higher-order’ interactions are assumed to be negligible or absent. Notably, these assumptions are poorly supported and have major consequences for the accuracy with which patterns of natural diversity are modelled and explained. We present a mathematically simple framework for incorporating biologically meaningful complexity into models of diversity by including non-additive higher-order interactions. We further provide empirical evidence that such higher-order interactions strongly influence species’ performance in natural plant communities, with variation in seed production (as a proxy for per capita fitness) explained dramatically better when at least some higher-order interactions are considered. Our study lays the groundwork for a long overdue shift in how species interactions are used to study the diversity of natural communities.
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Taxa co‐occurring in communities often represent a nonrandom sample, in phenotypic or phylogenetic terms, of the regional species pool. While heuristic arguments have identified processes that create community phylogenetic patterns, further progress hinges on a more comprehensive understanding of the interactions between underlying ecological and evolutionary processes. We created a simulation framework to model trait evolution, assemble communities (via competition, habitat filtering, or neutral assembly), and test the phylogenetic pattern of the resulting communities. We found that phylogenetic community structure is greatest when traits are highly conserved and when multiple traits influence species membership in communities. Habitat filtering produces stronger phylogenetic structure when taxa with derived (as opposed to ancestral) traits are favored in the community. Nearest‐relative tests have greater power to detect patterns due to competition, while total community relatedness tests perform better with habitat filtering. The size of the local community relative to the regional pool strongly influences statistical power; in general, power increases with larger pool sizes for communities created by filtering but decreases for communities created by competition. Our results deepen our understanding of processes that contribute to phylogenetic community structure and provide guidance for the design and interpretation of empirical research.
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Species introductions are a dominant component of biodiversity change but are not explicitly included in most discussions of biodiversity-disease relationships. This is a major oversight given the multitude of effects that introduced species have on both parasitism and native hosts. Drawing on both animal and plant systems, we review the competing mechanistic pathways by which biological introductions influence parasite diversity and prevalence. While some mechanisms - such as local changes in phylogenetic composition and global homogenization - have strong explanatory potential, the net effects of introduced species, especially at local scales, remain poorly understood. Integrative, community-scale studies that explicitly incorporate introduced species are needed to make effective predictions about the effects of realistic biodiversity change and conservation action on disease. Introduced species can have strong effects on parasite prevalence and richness.Introductions primarily affect disease via changes in host composition, not richness.Local effects of introduced species can amplify or dilute parasite prevalence.Global homogenization and spread of human commensals systematically increase disease.