Biol. Rev. (2018), 93, pp. 950–970.
doi: 10.1111/brv.12380
950
Towards an eco-phylogenetic framework
for infectious disease ecology
Nicholas M. Fountain-Jones1∗ , William D. Pearse2 , Luis E. Escobar1,3 ,
Ana Alba-Casals1 , Scott Carver4 , T. Jonathan Davies5 , Simona Kraberger6 ,
Monica Papeş7 , Kurt Vandegrift8 , Katherine Worsley-Tonks1 and Meggan E. Craft1
1
Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN 55108, U.S.A.
Ecology Center and Department of Biology, Utah State University, Logan, UT 84321, U.S.A.
3
Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA 24061, U.S.A.
4
School of Biological Sciences, University of Tasmania, Hobart 7001, Australia
5
Department of Biology, McGill University, Montreal H3A 1B1, Canada
6 Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO 80523, U.S.A.
7
Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN 37996, U.S.A.
8
Department of Biology, The Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA 16802, U.S.A.
2
ABSTRACT
Identifying patterns and drivers of infectious disease dynamics across multiple scales is a fundamental challenge for
modern science. There is growing awareness that it is necessary to incorporate multi-host and/or multi-parasite
interactions to understand and predict current and future disease threats better, and new tools are needed to help
address this task. Eco-phylogenetics (phylogenetic community ecology) provides one avenue for exploring multi-host
multi-parasite systems, yet the incorporation of eco-phylogenetic concepts and methods into studies of host pathogen
dynamics has lagged behind. Eco-phylogenetics is a transformative approach that uses evolutionary history to infer
present-day dynamics. Here, we present an eco-phylogenetic framework to reveal insights into parasite communities
and infectious disease dynamics across spatial and temporal scales. We illustrate how eco-phylogenetic methods can
help untangle the mechanisms of host–parasite dynamics from individual (e.g. co-infection) to landscape scales (e.g.
parasite/host community structure). An improved ecological understanding of multi-host and multi-pathogen dynamics
across scales will increase our ability to predict disease threats.
Key words: co-infection, ecological niche modelling, multi-host, multi-parasite, pathogens, phylodynamics, phylogenetic
community ecology, spill-over, transmission.
CONTENTS
I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(1) Phylogenetic ecology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(2) A guide to eco-phylogenetic methods and complementary tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(a) Bayesian phylodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(b) Co-phylogenetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(c) Distance-based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(d) Ecological niche modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(e) Phylo-diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(f ) Phylogenetic Generalised Linear Mixed Models (PGLMMs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
II. Co-infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
III. Transmission dynamics and pathogen gene flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(1) Eco-phylogenetic approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
* Address for correspondence (Tel: +1 (612) 626-8387; E-mail: nfj@umn.edu).
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(2) Application of an eco-phylogenetic framework to analyse drivers of pathogen transmission . . . . . . . . . .
IV. Multi-host complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
V. Parasite community structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
VI. Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(1) Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
(2) Pattern, process, and functional characterisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
VII. Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
VIII. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
IX. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
X. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
I. INTRODUCTION
(1) Phylogenetic ecology
Merging community and disease ecology is increasingly
considered necessary to understand infectious disease
dynamics (Johnson, de Roode & Fenton, 2015; Rynkiewicz,
Pedersen & Fenton, 2015; Seabloom et al., 2015). Hosts
are frequently infected by multiple parasites (even multiple
genotypes of the same parasite), and parasites often
circulate among multiple hosts (both intra- and inter-host
species) (Cleaveland, Laurenson & Taylor, 2001; Vaumourin
et al., 2015). Potential parasite–parasite and host–parasite
synergisms and antagonisms are not only important for
host recovery or mortality (e.g. Druilhe, Tall & Sokhna,
2005), but also can be a driving force in parasite evolution
and diversification (e.g. Vandegrift et al., 2010). There has,
therefore, been a shift in disease-driven research towards
encompassing multi-host and multi-parasite (MH–MP)
interactions; advancing the ‘one host one parasite’ paradigm,
which has provided basic theory essential for the field, but
which is limited in scope. While it is now increasingly
recognised that community ecology is critical for our
understanding of host–pathogen systems (Pedersen &
Fenton, 2007; Rigaud, Perrot-Minnot & Brown, 2010;
Johnson et al., 2015), a new framework is required to embrace
the ecological and evolutionary complexity of host–pathogen
dynamics across scales.
Over the last decade, a subdiscipline of ecology has
emerged with the potential to address the challenge of
MH–MP dynamics in disease biology. Advancement of
sequencing and phylogenetic methods, coupled with evolving
bioinformatic tools, has helped integrate community ecology
and phylogenetic biology, giving rise to the new field
of eco-phylogenetics (Webb et al., 2002). The merging of
these two previously separate fields has enabled a new
and greater understanding of the complex mechanisms
driving non-parasitic communities and is now a central
component of macroecology (Cavender-Bares et al., 2009).
While population genetics (e.g. phylodynamics, see Grenfell
et al., 2004; Archie, Luikart & Ezenwa, 2009) has facilitated
major inroads into understanding single-pathogen dynamics,
here we explain how eco-phylogenetics can further aid
progress in understanding MH–MP systems, from individual
to landscape scales (Fitzpatrick & Keller, 2015; Gilbert &
Parker, 2016).
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The integration of phylogenetics within community ecology has been transformative (Cavender-Bares et al., 2009;
Vamosi et al., 2009). Eco-phylogenetics was originally predicated on the simple assumption that more closely related
species will tend to resemble each other more closely in
their ecologies, physiologies, and life histories than more distantly related species (i.e. phylogenetic niche conservatism;
Wiens & Graham, 2005). Under this assumption, patterns
of phylogenetic clustering (co-occurrence of closely related
species) have been interpreted as evidence for environmental
filtering on evolutionarily conserved traits, whereas patterns
of phylogenetic over-dispersion (co-occurrence of more distantly related species) may be indicative of competitively
structured communities (Webb et al., 2002). At a broader
scale, the phylogenetic structure of regional species pools
provides additional information on historical biogeography
and lineage diversification (Davies & Buckley, 2011, 2012)
and helps to explain gradients in biodiversity (Wiens &
Donoghue, 2004). Even though there is debate about the
strength of phylogenetic niche conservatism for free-living
species across scales (e.g. Cavender-Bares et al., 2009; Mayfield & Levine, 2010), there is now a large and rapidly growing
body of evidence suggesting that phylogenetic conservatism
is an important feature of host–parasite systems (e.g. Davies
& Pedersen, 2008; Streicker et al., 2010; Poulin, Krasnov
& Mouillot, 2011; Huang et al., 2014; Parker et al., 2015;
Gilbert & Parker, 2016). Nonetheless, new eco-phylogenetic
approaches have been developed that relax the assumption of strict niche conservatism (Ives & Helmus, 2011), and
there is now a wide range of metrics and models applicable to
numerous systems (see Pearse et al., 2014; Tucker et al., 2017).
Eco-phylogenetic metrics can be grouped into three
broad components of phylogenetic diversity: richness (sum
of accumulated phylogenetic difference in a community),
divergence (average phylogenetic difference of taxa in a
community), and regularity (how regular the phylogenetic
differences are in a community) (Tucker et al., 2017). Each
phylo-diversity (see Section I.2e) component can be applied
to examine patterns within sets of communities (α diversity)
and between sets of communities (β diversity), with each
component suited to different types of ecological questions
(see Tucker et al., 2017). Such metrics are now commonplace
in the plant and animal ecology literature; however, the
application of eco-phylogenetic approaches into other fields,
such as disease ecology, has lagged behind. Gilbert & Parker
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(2016) review the current understanding of phylogenetic
patterns of disease risk in plants, but a more general synthesis
across scales and taxa is needed.
Eco-phylogenetic approaches coupled with population
genetic and macroecological approaches such as ecological
niche modelling (see Section I.2d), have the potential
to expand insights into host–parasite dynamics across
spatial scales. In non-parasite communities, the fusion
of phylogenetics and ecological niche modelling has
enabled investigations of environmental niche conservatism
(Peterson, Soberón & Sánchez-Cordero, 1999; Wiens
& Graham, 2005; Losos, 2008; Peterson et al., 2011),
speciation in relation to environmental heterogeneity,
biogeographic barriers, refugia, and non-analogous climates
and communities (Bonaccorso, Koch & Peterson, 2006;
Peterson & Nyári, 2008; Serra-Varela et al., 2015). Emergent
methods in ecological niche modelling include adding
phylogenetic dimensions (e.g. Morales-Castilla et al., 2017)
with the goal of understanding the role of environmental
and geographic distances in diversification patterns (Rissler
& Apodaca, 2007; Alvarado-Serrano & Knowles, 2014;
Lira-Noriega & Manthey, 2014). Phylogenetic niche conservatism additionally suggests potential for reciprocal niche
estimation of species that are phylogenetically close, or that
have co-evolved (e.g. host and parasite). Eco-phylogenetic
tools and ecological niche modelling, however, have rarely
been used together to gain insights into ‘infra-communities’
of parasites and symbionts (but see Poulin et al., 2011).
While phylogenetic patterns of hosts and parasites can
greatly increase our understanding of infectious disease
dynamics (Archie et al., 2009), there are no established
frameworks that can unify both components across scales. For
example, in some cases host–parasite associations can have
a strong phylogenetic structure, such that host phylogeny
can predict disease spill-over and prevalence (Streicker
et al., 2010; Parker et al., 2015). Experimental inoculations
of tropical forest trees with fungal parasites have shown
that the fungi have restricted phylogenetic host ranges, such
that the likelihood of a parasite infecting two tree species
declines with increasing phylogenetic distance (Gilbert &
Webb, 2007). For some primates and carnivores, it has been
documented that the similarity in some parasite communities
among species is predicted well by their hosts’ phylogenetic
relationships, whereby close relatives tend to harbour similar
suites of parasites (Davies & Pedersen, 2008; Huang et al.,
2014). In addition, parasite phylogeny can provide a rich
source of information about infectious disease dynamics
by providing insights into parasite biogeography and spread,
particularly for viruses and bacteria (e.g. Biek et al., 2012). For
example, identifying areas of high pathogen phylo-diversity
may be important for understanding novel disease emergence
(Barton et al., 2014) and more broadly can be used to infer
the evolutionary mechanisms underlying parasite community
assembly (Krasnov et al., 2014; Scordato & Kardish, 2014).
Here, we highlight how eco-phylogenetics can play a key
role in advancing our knowledge of disease ecology across
a hierarchy of scales (Fig. 1). Such advances are urgently
Biological Reviews 93 (2018) 950–970 © 2017 Cambridge Philosophical Society
Nicholas M. Fountain-Jones and others
needed as emerging and re-emerging infectious diseases
are increasing in frequency and distribution, and constitute
a major public, animal, and plant health threat (Daszak,
Cunningham & Hyatt, 2000; Jones et al., 2008; Tompkins
et al., 2015), however our ability to forecast accurately where
and when these emergence events will occur is limited (for
examples see Jones et al., 2008; Pedersen & Davies, 2009).
We suggest, in agreement with Johnson et al. (2015), that
the lack of accurate predictions is, in part, because infectious
diseases are often considered in isolation, when in reality they
are embedded within a complex and interactive community.
Importantly, we argue that eco-phylogenetic techniques can
help address four linked challenges in infectious disease
ecology (Fig. 1), spanning from within-host to landscape
scales.
The first challenge to address is to understand co-infection
better within hosts. Co-infections introduce at least one
additional layer of complexity as parasites can interact with
each other directly or indirectly via the immune system, and
co-infecting parasites may have inhibitory (e.g. Blackwell
et al., 2013) or facilitative effects upon one another (e.g.
Syller, 2012). Co-infections are ubiquitous, yet remain
understudied (Cattadori, Boag & Hudson, 2008; Susi et al.,
2015). The second challenge concerns parasite gene flow
and transmission mode where critical questions include:
what are the underlying landscape, environmental, and host
determinants of parasite gene flow; and which transmission
pathway (e.g. horizontal or vertical) is the most important for
the parasite? The third challenge is to incorporate multi-host
complexes (i.e. when a parasite infects multiple host species)
into our understanding of host–pathogen dynamics. This
will be critical for predicting when and where the risks of
spill-over are highest, and for extrapolating epidemiologically
relevant parameters across multiple host species. It is also
possible that understanding the phylogenetic dimensions
of host community assembly could help gain insights into
dilution effects (e.g. Keesing, Holt & Ostfeld, 2006). The final
challenge deals with parasite community structure across the
landscape, and is aimed at identifying hotspots and drivers
of parasite diversity, and parasite community assembly rules,
which are in part driven by host community assemblages.
Each of these challenges and associated questions are of
fundamental importance to understanding host–pathogen
dynamics across scales, yet they remain largely unaddressed
(Mouillot et al., 2005; Scordato & Kardish, 2014; Seabloom
et al., 2015). Here, we show how eco-phylogenetic approaches
can contribute to addressing these challenges, and link scales
from individual to landscape in a unified framework.
(2) A guide to eco-phylogenetic methods and
complementary tools
(a) Bayesian phylodynamics
Bayesian phylodynamics methods are commonly employed
to understand parasite transmission dynamics and
phylogeography predominantly using coalescent-based
estimation and Bayesian Markov chain Monte Carlo
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Fig. 1. Conceptual schema illustrating how an eco-phylogenetic framework can be applied to understand infectious disease
dynamics. The example system used is the Ngoronogoro Crater (Tanzania), across scales: (A) within host, (B) among hosts of the
same species, (C) multi-host complex, and (D) landscape scale. Colour-coded and lettered symbols below each panel indicate what
data (squares) and statistical tools (circles) could be used to address each challenge (see Section I.2 for model and other tool details).
White ovals contain hypothetical parasite communities within a host and different parasite colours and shapes (nematodes or viruses)
represent different parasite species or genotypes. PGLMM: phylogenetic generalised linear mixed model.
(MCMC) methods. Useful software packages include BEAST
software architecture (Drummond & Rambaut, 2007).
In addition, SERAPHIM (Dellicour et al., 2016) can
analyse how a landscape can alter parasite movement by
extracting spatio-temporal information from phylogenetic
reconstructions.
(b) Co-phylogenetics
A set of methods that enable host–parasite co-speciation
and host shifts to be inferred by comparing host and
parasite phylogenetic relationships. If both host and parasite
phylogenies are congruent, co-speciation is likely to be
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the dominant evolutionary process, whereas mismatches
between the two may be evidence for a host shift (i.e.
when a parasite population adapts to a new host and
speciates). Co-phylogenetic methods can be either ‘event
based’ where the number and frequency of co-speciation
events is estimated (e.g. if there are more co-speciation events
compared with a null model), or ‘topology or distance based’
methods where the symmetry or similarity of the phylogenies
is compared (e.g. high similarity may indicate co-speciation
events). See de Vienne et al. (2013) for a comprehensive
review of co-phylogenetic methods.
(c) Distance-based methods
A very broad array of analyses (often non-parametric)
that can be applied to phylogenetic distance or
phylogenetic weighted community (dis)similarity matrices
and use permutation or Monte-Carlo methods to estimate
significance of effects. Includes tests such as permutational
ANOVA (PERMANOVA) that tests for differences between
groups (Anderson, 2001) and multiple regression approaches
such as distance-based linear modelling (DISTLM) and
generalised dissimilarity modelling (GDM; Ferrier et al.,
2007; Rosauer et al., 2014). These methods are particularly
useful if the aim of the study is to understand what
factors shape parasite phylogenetic patterns. Useful software
packages include: R package ‘vegan’ (Oksanen et al., 2013)
and PRIMER PERMANOVA+ (Anderson, Gorley &
Clarke, 2008).
(d) Ecological niche modelling
An approximation of species’ ecological niches based on
analytical methods that link the geographic presences (or
presences and absences) of a species with abiotic constraints
(environmental conditions like climate and landscape
structure). For example, Maxent is a presence–background
correlative method that uses species’ presences and large
samples of the background of the study area (Phillips,
Anderson & Schapire, 2006); other methods conceptually
use presence–absence data: General Linear Models (GLM),
General Additive Models (GAM), and Boosted Regression
Trees (BRT); these methods are available in the R package
‘dismo’(Hijmans et al., 2017). Other strictly presence-only
methods, based on the position of the presences in
environmental dimensions, include NicheA (Qiao et al.,
2016), Marble (Qiao et al., 2015), and hypervolume kernel
density estimation (Blonder et al., 2014).
(e) Phylo-diversity
Metrics of diversity that extend species-level diversity
(species richness, divergence, and evenness) to consider
phylogenetic non-independence of species: species do not
equally represent all fractions of the tree of life. Common
α phylo-diversity metrics, i.e. that measure phylogenetic
richness in a community, include Faith’s Phylo-diversity (PD
- the sum of all branch lengths on a phylogeny; Faith,
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Nicholas M. Fountain-Jones and others
1992), whereas Mean Phylogenetic Distance (MPD; the
mean pairwise distance among all species on a phylogeny)
and Mean Nearest Taxonomic Distance (MNTD; the mean
nearest-neighbour distance of all species on a phylogeny)
are common phylogenetic divergence metrics. Common
β-diversity metrics, comparing phylogenetic richness
between communities, such as UniFrac and PhyloSor (Bryant
et al., 2008), are based around shared branch lengths, and
phylogenetic community dissimilarity (PCD; Ives & Helmus,
2010) is notable for decomposing diversity into phylogenetic
and non-phylogenetic components. There are almost as
many reviews of phylo-diversity as there are metrics; recent
approaches have grouped metrics according to the kinds
of data they require (Pearse et al., 2015) and what kinds of
ecological questions they can answer (Tucker et al., 2017).
(f ) Phylogenetic Generalised Linear Mixed Models (PGLMMs)
An extension of mixed-effects modelling approaches (Bolker
et al., 2009), regressing species (parasite) abundance or
presence/absence as a function of environmental conditions
and species’ traits (Ives & Helmus, 2011). Random effect
parameters can detect phylogenetic patterns in species’
distributions, and can be extended to model both hosts
and parasites (see Rafferty & Ives, 2013).
II. CO-INFECTION
Understanding within-host infection patterns is a fundamental challenge for disease ecology (Buhnerkempe et al., 2015).
Diverse organisms co-occur and interact within the same
host, but within-host parasite communities are not commonly
investigated using eco-phylogenetic methods (Cox, 2001;
Steinmann & Du, 2010; Rynkiewicz et al., 2015; Seabloom
et al., 2015). We define co-infections as multiple infections of
unrelated pathogens or between distinct taxonomic units of
the same pathogen in the same host. Numerous studies have
emphasised the importance of co-infections, showing that
host immune response to parasites and ecological interactions between parasites can facilitate or limit infections with
other parasites (Cox, 2001; Rynkiewicz et al., 2015), and that
the effects can be reciprocal or asymmetrical. For example,
in field voles (Microtus agrestis), Telfer et al. (2010) described
both positive and negative associations among micro-parasite
species (a virus, a protozoan, and two bacteria) and showed
that the co-infections were stronger predictors of infection
risk than other variables such as exposure risk and host
condition. Parasite interactions are commonly explored by
assessing ‘top-down’ (host immune system) and ‘bottom-up’
(host resources) control mechanisms (Pedersen & Fenton,
2007) using patterns of exposure/infection or co-occurrence.
Here, we suggest that an eco-phylogenetic framework can
expand this approach by incorporating phylogenetic relatedness of the within-host community across scales.
Patterns of phylo-diversity have been particularly
important in understanding the mechanisms behind species
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Fig. 2. Schematic diagram showing three differing hypothetical phylo-diversity patterns in co-infection in three locations in the
human gastro-intestinal tract. (A) Co-infection of the oral cavity involving three viruses, each from phylogenetically distinct clades
indicating that these viruses may have a relatively long evolutionary relationship with the host (Thézé et al., 2015). We hypothesize
that the viral community in this case is likely to be stable due to competition for resources as there is high phylogenetic divergence
and regularity. New viral invasions might be less likely. (B) Highly phylogenetically divergent but irregular co-infection in the small
intestine involving four nematode species. Even though there is high phylogenetic divergence, the low phylogenetic regularity of
this co-infection may indicate that this co-infection is not stable, yet infection by other nematode species may be less likely due
to resource competition. (C) Phylogenetically clustered (low divergence and regularity) co-infection in the large intestine, which
could be evidence for environmental filtering (e.g. lower nutrient levels in this organ), or unoccupied niche space. If the clustering
represents unoccupied niche space, invasion by phylogenetically distinct species may be more likely.
coexistence of non-parasite communities, and thus have
the potential to reveal new insights into the patterns of
co-infection. For example, plant communities that have
high phylogenetic divergence and regularity were much
more stable over time compared to plant communities with
low divergence and regularity (e.g. Cadotte, Dinnage &
Tilman, 2012). Conversely, resource stability may lead to
high phylo-diversity, as has been shown in a variety of
plant and animal systems (e.g. Graham et al., 2009; Whitfeld
et al., 2012). Furthermore, insights from invasion ecology
demonstrate that invasion success of novel species is less
likely in communities with high phylogenetic richness (Davis,
Grime & Thompson, 2000; Whitfeld et al., 2014). We argue
that the same type of phylo-diversity relationships may apply
to co-infected hosts (Fig. 2). To our knowledge, the only study
that has used phylo-diversity metrics to examine parasite
co-occurrence found that ectoparasitic co-infections were
more common between related flea species (phylogenetic
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clustering) (Krasnov et al., 2014). Experimental work has
also shown that more-related parasite strains were more
likely to co-infect a plant host (Koskella, Giraud & Hood,
2006), although this effect was not quantified. Whether these
finding are generalizable to other systems and other locations
in the host is unknown. Analysing phylo-diversity patterns
across multiple locations within the host (e.g. the large
versus small intestine, Fig. 2) for a cross section of the host
population may allow for generalisations for how species that
form co-infections assemble. For example, the lower nutrient
content of the large intestine may hypothetically lead to
co-infections with high parasite relatedness (low divergence)
that are similar across a host population or group of host
species. The species that constitute the co-infection could
be different but the environmental conditions could lead to
the same pattern. Future empirical work should examine
and manipulate the phylogenetic structure of co-infecting
parasite communities to determine if such generalities exist,
although such experimental manipulations can be difficult to
achieve (see Section VI).
What organisms to include in these analyses, and
at what taxonomic scale, are important considerations,
as eco-phylogenetic inferences are sensitive to both
phylogenetic and spatial scale (e.g. Cavender-Bares et al.,
2009). For example, including unrelated symbiotic bacteria
(the microbiome), as well as nematodes from the two
locations in the gut (Fig. 2), or increasing the spatial scale
by lumping both intestinal communities together, would
likely influence clustering patterns, and thus change the
eco-phylogenetic inference drawn. These are general and
well-recognised issues in phylogenetic ecology, and whilst
there are no simple answers as to which scale is the most
appropriate, there are some general guidelines that can
help. In essence, the location sampled has to be small
enough for individuals to interact, and the taxonomic units
related enough that competition is a plausible explanation
(the Darwin–Hutchinson zone, see Vamosi et al., 2009 for
a more detailed discussion). In poorly understood host
co-infection systems, identifying the Darwin–Hutchinson
zone may be challenging, however being aware of potential
biases associated with scale-sensitivity might not only help
guide study design, but also highlight the relative importance
of different processes operating across scales (see Cadotte &
Davies, 2016).
III. TRANSMISSION DYNAMICS AND
PATHOGEN GENE FLOW
(1) Eco-phylogenetic approaches
Transmission dynamics among individuals, more broadly,
pathogen and parasite gene flow at a landscape scale, are
still poorly understood, mostly because data are sparse and
difficult to collect, particularly for wild animal populations.
Here, we define ‘transmission’ as the process of transmitting
a pathogen from one individual to another, ‘spread’ as the
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Nicholas M. Fountain-Jones and others
geographic dispersal of the pathogen (Lemey et al., 2009), and
‘gene flow’ as the signature of past transmission events across
a landscape. Previous studies of transmission in wildlife
have relied on intensive field studies (e.g. construction
of transmission networks by studying contact networks;
Grear, Luong & Hudson, 2013) or have been restricted to
purely theoretical explorations (e.g. network simulations and
mechanistic modelling; Clauset, Moore & Newman, 2008).
Evolutionary approaches are starting to be used to help fill
this gap (e.g. for Ebola; Carroll et al., 2015). In particular,
due to high rates of evolution, viral genetic data collected
from host populations can provide fundamental insights into
the transmission dynamics and gene flow of parasites. With
greater sequencing effort, however, evolutionary approaches
can also be utilised with non-viral agents that evolve at
slower rates. For example, high-resolution whole-genome
sequencing can reveal transmission dynamics of bacteria
(Biek et al., 2012; Kao et al., 2014; Kamath et al., 2016),
but this can be prohibitively expensive for large numbers
of samples (see Section VI). Integrated population genetic
approaches, such as the field of phylodynamics (Grenfell et al.,
2004), can link demography and evolution of a parasite,
thereby providing important insights into the gene flow
of directly transmitted parasites (Biek et al., 2007; Biek &
Real, 2010; Lee et al., 2012). This approach, however, is
technically challenging (Dellicour, Rose & Pybus, 2016).
Eco-phylogenetic approaches are already well developed,
but still remain under-utilised in tackling the drivers of
parasite gene flow.
Eco-phylogenetic approaches, particularly distance-based
methods (see Section I.2c), could provide insights into
two aspects of transmission, specifically: how gene flow is
shaped by multiple interacting factors and transmission mode
across multiple scales. Approaches commonly employed
to examine the environmental and biotic determinants of
phylo-β diversity (see Section I.2c) could be readily applied to
parasite patristic distance (i.e. pairwise sum of branch lengths)
or phylogenetic dissimilarity matrices. Machine-learning
techniques, such as gradient forests (Ellis, Smith & Pitcher,
2012), also offer possibilities to reconstruct pathogen gene
flow by analysing the host genetic data [single nucleotide
polymorphism (SNP) matrix] (Fitzpatrick & Keller, 2015).
Gradient forests are well suited to analyse how large sets of
complex predictor variables shape gene flow as the technique
is less sensitive to variable collinearity than more traditional
approaches (Ellis et al., 2012). Furthermore, techniques such
as gradient forests or GDM can be used to make spatial
predictions about pathogen gene flow (Fitzpatrick & Keller,
2015) that can be used to forecast parasite spread in a
landscape.
Combined, eco-phylogenetics and phylodynamics have
significant scope to extend insights into pathogen gene
flow and transmission mode dynamics between hosts
more broadly. We envision a landscape phylodynamic
approach where Bayesian phylodynamic methods (Section
I.2a) estimate pathogen transmission and spread through
time and space (Lemey et al., 2009, 2010) and how landscape
Eco-phylogenetics and disease ecology
957
Fig. 3. A theoretical example of how eco-phylogenetic techniques combined with Bayesian phylogeography (BEAST) could be
used to analyse pathogen transmission dynamics in wildlife based on pathogen phylogenetic relationships. (A) An example of how
both eco-phylogenetic and phylodynamic methods could be used to incorporate spatio-temporal data, landscape variables, host
relatedness, host variables, and contact network data to understand pathogen transmission dynamics between pumas (Puma concolor)
(individual pumas labelled a to e). Statistical techniques are italicised (see Section I.2 for method details). Stars represent an estimated
transmission event [within a period defined by high probability density (HPD) intervals]. (B) Inferences on transmission dynamics of
the pathogen in the P. concolor population. In this example, two transmission events were based on phylogeographic estimates (a to
c for 2004–2007 and c to b in 2007 (both based on 95% HPD estimates). Circles around the stars in the landscape indicate 95%
HPD interval for the location of each transmission event in the landscape. Dotted line connecting puma d and e indicates that there
was pathogen gene flow in the past but transmission directly between these individuals may not have occurred due to uncertainty
in temporal estimates (i.e. it is possible that other individuals were intermediaries in the transmission chain). Overall, based on the
multivariate distance-based methods, forest cover and host relatedness were the best predictors of gene flow in this hypothetical
scenario.
factors shape parasite movement (Dellicour et al., 2016).
Eco-phylogenetic approaches could provide evidence related
to how landscape and host variables interact to influence
pathogen gene flow (Fig. 3) and forecast areas where gene
flow, and thus future transmission events, are likely to occur in
the future. For example, in Fig. 3, phylodynamic techniques
can be used to derive spatio-temporal estimates for the two
likely transmission events (between individuals a–c and c–b)
and to show how fast the parasite is spreading in the landscape
(Dellicour et al., 2016). Eco-phylogenetic techniques could
show that an interaction between host relatedness and forest
cover best explained gene flow and these variables could be
used to predict future gene flow (Fig. 3).
Using similar tool sets but different types of data,
eco-phylogenetic analyses can also be used to untangle
transmission mode (i.e. how a parasite is transmitted). For
example, distance-based regression techniques could be used
to assess if parasite phylogenetic distance between known
host parent–offspring relationships is less than expected by
chance (Fountain-Jones et al., 2017). If geographic distance
or the host contact network (Fig. 3) is a more important
predictor of parasite phylogenetic distance, as compared
to host genetic distance, this could provide evidence for
horizontal transmission. Below we provide a case study
where similar techniques were applied to untangle disease
transmission dynamics and gene flow in a bobcat (Lynx
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Nicholas M. Fountain-Jones and others
Fig. 4. Summary of data and eco-phylogenetic techniques used to understand two interlinked questions. (A) Feline immunodeficiency
virus (FIV) single nucleotide polymorphisms (SNPs) were predicted by bobcat relatedness, and demographic and landscape variables.
This was achieved using gradient forest regression models in order to assess how these factors shape gene flow. See Section III.1 for
information about gradient forests multiple regression. The box on the far upper right shows the urban landscape south west of Los
Angeles, California with grey shading representing degree of urban development (% impervious surface). The black line indicates
the position of a highway that restricts host gene flow in this system (see Lee et al., 2012). (B) FIV phylogeography using bobcat
spatial location and sampling dates as well as demographic factors. This was achieved using BEAST and SERAPHIM in order to
assess how, where, and when FIV was transmitted. See Section I.2a for information on BEAST phylogeography and SERAPHIM.
Coloured symbols reflect different individual bobcats.
rufus) population in California. A similar approach could
be employed to assess the impact of social networks on
disease. For example, for directly transmitted parasites, the
impact of social organisation could be assessed by comparing
within-group parasite phylogenetic distance to that between
groups. Distance-based regression methods could also
augment what has been done to understand the social
component of Escherichia coli transmission in multiple wildlife
species (Chiyo et al., 2014; VanderWaal et al., 2014). Bayesian
discrete trait analysis (Drummond & Rambaut, 2007) could
be further employed to assess how frequently between-group
transmission events were likely to have occurred.
(2) Application of an eco-phylogenetic framework
to analyse drivers of pathogen transmission
Fountain-Jones et al. (2017) applied a gradient forest
approach (Fitzpatrick & Keller, 2015) coupled with Bayesian
Biological Reviews 93 (2018) 950–970 © 2017 Cambridge Philosophical Society
phylogeography to disentangle the effects of urbanisation on
pathogen gene flow and transmission in bobcat populations
around Los Angeles, California. As part of this study 106
bobcats were sampled and tested for feline immunodeficiency
virus (FIV), of these 19 were positive. Positive samples
were then sequenced (see Lee et al., 2012), and gradient
forest methods were applied to relate host traits (e.g. sex),
relatedness (based on microsatellite loci), and landscape
features with FIV SNPs, a surrogate for virus gene flow
(Fitzpatrick & Keller, 2015). Fig. 4 provides a summary of
the data and eco-phylogenetic tools used to answer each
linked question.
This analysis revealed that landscape variables that shaped
host gene flow, such as grassland and forest, combined with
host relatedness, also shaped FIV gene flow. Finer scale
Bayesian phylogeographic estimates of transmission event
locations confirmed that transmission was more likely in
Eco-phylogenetics and disease ecology
areas with less human activity, even in this heavily urbanized
system (Fountain-Jones et al., 2017).
Whilst this study provides new insights into transmission
of a pathogen in a secretive species’ population, that would
have been hard to determine otherwise, it also highlights
some of the methodological difficulties that still need to
be resolved. For instance, the spatial data used in the
models were based on individual capture location which
does not necessarily represent individual movement, and
this could bias phylogeographic estimates. In addition,
non-independence of genetic data arising from linkage
disequilibrium can complicate interpretation of analyses
using methods such as gradient forest (Fitzpatrick &
Keller, 2015). Nonetheless, methodological improvement
is occurring rapidly, and will greatly increase the efficacy of
these methods in the future.
IV. MULTI-HOST COMPLEX
While there has been increased attention on understanding
co-infection, it is now also widely recognised that most
parasites are multi-host, i.e. they infect and are transmitted
within several host species that may differ in both
susceptibility and infectiousness (Cleaveland et al., 2001;
Woolhouse, Taylor & Haydon, 2001). One key challenge
is the identification of reservoir species hosts that maintain
parasite populations, which may then spill over into other
host species (Haydon et al., 2002; Streicker, Fenton &
Pedersen, 2013; Johnson et al., 2015; Viana et al., 2015; Han,
Kramer & Drake, 2016). To address this challenge often
requires long-term observational, experimental (Johnson
et al., 2015), and/or theoretical modelling studies (e.g.
O’Hare et al., 2014). The process of pathogen spill-over
to a new host is a major concern as most emerging infectious
diseases in humans have zoonotic origins (Woolhouse &
Gowtage-Sequeria, 2005) including strains of pandemic
influenza (Vandegrift et al., 2010), West Nile virus (Kilpatrick
et al., 2006), and Ebola (Leroy et al., 2005). Spill-over between
species is often not phylogenetically random (Kuiken et al.,
2006; Streicker et al., 2010), and thus eco-phylogenetic
methods should also provide insights into the processes
underlying these spill-over events.
Host–parasite communities can persist over evolutionary
periods and thus comparing host and parasite phylogenies
directly can help to understand the temporal dimension of
pathogen spill-over. One of the most intuitive approaches
is direct mapping of the parasite tree onto the host
tree or co-phylogenetics (see Section I.2b), with the goal
of identifying co-speciation and spill-over events (e.g.
Charleston & Robertson, 2002; Brooks & Ferrao, 2005;
Switzer et al., 2005; Ramsden, Holmes & Charleston,
2008; Firth et al., 2010). Co-phylogenetic approaches have
been applied to a wide variety of pathogen–host systems
and have revealed important insights into host–pathogen
co-evolution at a macroevolutionary scale. For example,
co-phylogenetic analysis of hantavirus–host relationships
959
revealed little evidence for co-speciation with rodent and
insectivore hosts and it is likely that recent spill-over events
have shaped hantavirus evolution (Ramsden et al., 2008).
By contrast, co-speciation was found to be important for
Simian foamy viruses in primates with highly congruent
branching orders and divergence times (Switzer et al., 2005).
Overall, however, spill-over could be the dominant force
shaping host–pathogen evolution (see de Vienne et al.,
2013 for a review of the literature). Co-phylogeny alone,
however, cannot help predict spill-over events or provide
direct insight into the mechanisms driving host shifts and
spill-over (Penczykowski, Laine & Koskella, 2016).
Differences in parasite host breadth may provide
information on the mechanisms underlying parasite host
shifts and spill-over. The probability of cross-species
transmission may be largely determined by host contact rates,
but the outcome of such events can vary widely, from single
infection spill-over events (e.g. West Nile Virus in humans;
Lloyd-Smith et al., 2009), to transient outbreaks (Wolfe,
Dunavan & Diamond, 2007; Lloyd-Smith et al., 2009), to
sustained outbreaks in the new host (e.g. rabies; Streicker
et al., 2010). The taxonomic host breadth of a parasite is
likely a product of attributes intrinsic to both parasite and
host biology. Key parasite traits include transmission mode,
genetic diversity, and evolutionary rates (e.g. high genetic
diversity or rapid evolutionary rates; Fenton & Pedersen,
2005, but see Streicker et al., 2010). Whether a parasite is
transmitted via a vector, through the environment, or by close
contact might also impact host breadth, although there is
likely an interaction between parasite type and transmission
mode (Fenton & Pedersen, 2005). On the other hand, a
number of empirical and theoretical studies have shown that
certain host species have specific behavioural, physiological,
and genetic traits that allow the parasite to spread rapidly
and persist within communities (Cleaveland et al., 2001;
Kilpatrick et al., 2006). However, measurements of such traits
are often lacking (see Section VI). Phylogenetic structure of
host communities can not only provide a surrogate for traits,
but also provide insights into the evolutionary mechanisms
shaping parasite–host interactions.
Host specificity, or the extent to which a parasite
can exploit different hosts (Poulin et al., 2011), is one
of the few areas where eco-phylogenetic metrics have
been employed to understand host–parasite systems.
Studies incorporating host specificity have provided novel
insights into primate–parasite co-evolution; for example, the
majority of parasites infecting primates were phylogenetic
generalists that infected more distantly related primate
hosts than was expected by chance (Cooper et al., 2012).
By contrast, for Drosophila sigma viruses, host specificity
was predicted by host phylogeny, with host switching
only between related hosts (phylogenetic clustering, low
divergence; Longdon et al., 2011). In both cases, however,
taxonomic richness (i.e. number of hosts a parasite
can exploit) would not have provided resolution of the
evolutionary drivers of these multi-host complexes. By
gaining a better understanding of host specificity and
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differences in host-specific virulence, we can then generate
more accurate models of parasite persistence within
multi-host communities (Poulin et al., 2011; Fenton et al.,
2015).
The phylo-diversity of the pathogen can also help identify
the direction of spill-over events from previously collected
sequence data, information that may be particularly valuable
when sample size of pathogens is low and nucleotide
substitutions rates are variable. If sample sizes across multiple
hosts are comparable, Bayesian discrete trait methods
(Drummond & Rambaut, 2007) can accurately trace
multi-host dynamics (e.g. Kamath et al., 2016). However,
if sample sizes are uneven, these methods are likely to
assign the species with the most samples as the reservoir
host in a particular system (Beerli, 2004; Viana et al.,
2014). Standardised phylo-diversity indices may be helpful in
identifying host reservoirs in complex communities. It is likely
that a parasite will have a greater evolutionary history in the
adapted (reservoir) host, therefore reservoir species may have
much higher pathogen phylo-diversity (greater phylogenetic
dispersion) compared to spill-over hosts (non-reservoir hosts
receiving spill-over pathogens from a reservoir host), where
phylogenetic clustering is more likely (see Fig. 5). As measures
like MPD and MNTD (see Section I.2e) are standardised
by number of samples (i.e. they compare the observed
phylo-diversity to the diversity expected by chance for a
given sample size), they may offer a useful solution for
unbalanced data sets, though empirical testing is required.
At a host community level, the phylogenetic structure
and diversity of hosts may provide valuable insights
into pathogen–host diversity relationships, although the
phylogenetic dimensions of diversity have rarely been
considered in this context (Gilbert & Parker, 2016). The
dilution effect hypothesis, for example, is based on the
premise that a host community with high α diversity dilutes
individual infection risk by providing a greater density of
incompetent hosts (Ostfeld & Keesing, 2000; Keesing et al.,
2006); thus biodiversity loss (or community disassembly)
may increase disease prevalence assuming that the most
competent hosts remain in the community. Even though
recent work has shown some support for the dilution effect
(e.g., Civitello et al., 2015), it is not clear how generalisable
it is and there are documented cases illustrating that
diversity can increase (amplification effect) disease risk (e.g.
Mitchell, Tilman & Groth, 2002). Host relatedness is likely
to play a role in multi-host systems (Parker et al., 2015),
and thus eco-phylogenetic metrics may help elucidate the
mechanisms behind the dilution and amplification effects (for
a hypothetical example of avian influenza, see Fig. 6; Gilbert
& Parker, 2016). For example, two communities may have
identical species richness, but high phylogenetic clustering of
hosts in one community may lead to amplification, as related
hosts might be more likely to be competent vectors and may
be more likely to come into contact with each other (Fig. 6,
Scenario 1). The opposite could be true for a phylogenetically
dispersed host community as prevalence could be diminished
via the dilution effect (Fig. 6, Scenario 2); and, of course,
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Nicholas M. Fountain-Jones and others
transmission between distant hosts would be less likely if
the parasites had co-evolved with their hosts. Even though
these ideas have yet to be fully tested on empirical data,
it seems likely that eco-phylogenetic patterns can provide
an important tool set to understand the mechanisms of
biodiversity–disease relationships.
V. PARASITE COMMUNITY STRUCTURE
Although parasites are non-randomly distributed across
landscapes and host species, we have a poor understanding
of the drivers of parasite diversity across space and hosts. Of
the various aspects of diversity, phylo-diversity is often linked
with ecosystem performance (e.g. Srivastava et al., 2012),
implying that hotspots of phylo-diversity might be associated
with disease outbreaks. For example, locations harbouring
genetically diverse parasites (measured by Simpson’s Index,
or other measures) are linked to greater risk of disease
emergence, particularly for viruses, due to the increased
likelihood of evolutionary innovations (Barton et al., 2014).
Because phylo-diversity might capture features of diversity
better than simple measures of species richness (Faith, 1992),
we might also predict that phylo-diversity would be a better
predictor of disease emergence via evolutionary innovation. If
a parasite community has high phylogenetic divergence we
hypothesise that the possible reassortment/recombination
event may be novel. However, if a viral community,
for example, is both highly divergent and irregular, it is
possible that biological limitations could be imposed on
recombination (Pérez-Losada et al., 2015). Eco-phylogenetic
metrics and models, such as the PGLMM (see Section I.2f ),
allow for the estimation of where highly phylogenetically
divergent parasite communities could be, and what particular
set of host or environmental variables underlie these diversity
patterns. However, eco-phylogenetic methods are currently
reliant on the ability to detect species consistently, which can
often be a large challenge for parasites, particularly when a
high proportion remains undiscovered (see Section VI).
Phylo-β diversity (variation in phylogenetic composition
of a parasite community) among sites or host populations
in a geographic area (Legendre, Borcard & Peres-Neto,
2005; Graham & Fine, 2008) can also reveal insights into
how abiotic and biotic gradients (biotic interactions among
hosts and within hosts) shape parasite communities. Patterns
of taxonomic β diversity have helped untangle the role
of geographic distance versus abiotic niche in structuring
parasite communities across the world (Poulin & Mouillot,
2003; Krasnov et al., 2005; Timi, Lanfranchi & Luque, 2010;
Scordato & Kardish, 2014; Warburton, Kohler & Vonhof,
2016). For example, helminth taxonomic β diversity in big
brown bat (Eptesicus fuscus) populations was better predicted
by abiotic variables compared to distance between bat
populations or host variables (sex and age), likely because bat
populations that experience the same abiotic variables were
more likely to be exposed to the same helminth communities
(Warburton et al., 2016). Incorporating measures of phylo-β
Eco-phylogenetics and disease ecology
961
Fig. 5. Schematic diagram illustrating a hypothetical scenario where standardized phylo-diversity measures could be used to help
infer reservoir species and the direction of spill-over events for a parasite data set with unbalanced sampling. The hypothetical
phylogenetic trees represent the relationships between bovine tuberculosis (bTB) isolates from badgers, ferrets, and a cattle herd,
where more badgers were sampled than other host species. Phylogenetic tree (A) is a scenario where both cattle and badger samples
have high values of phylogenetic richness, so there is no evidence for either species being the reservoir (bottom panel). By contrast,
phylogenetic tree (B) illustrates a scenario where cattle samples had a much lower relative phylogenetic richness compared to the
badger samples, indicating that badgers may be the reservoir host (bottom panel). In both scenarios ferrets are unlikely to be the
reservoir as they have low phylogenetic richness in both trees.
Biological Reviews 93 (2018) 950–970 © 2017 Cambridge Philosophical Society
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Nicholas M. Fountain-Jones and others
Fig. 6. The potential for an influenza strain to persist in two hypothetical avian communities that differ in the degree of host
relatedness: (A) Phylogenetic tree of five avian orders. (B) Two host communities (Scenario 1 and Scenario 2) that differ in the
degree of host phylogenetic divergence, and how this might influence pathogen persistence and prevalence. Host species richness is
the same in both scenarios, however, host species are more closely related in Scenario 1 than in Scenario 2. The low divergence
(phylogenetic clustering) in Scenario 1 may increase influenza prevalence over time in this community as contacts between related
species (and thus between competent hosts) might happen more frequently than between distantly related species. If there are strong
co-evolutionary relationship(s) between a particular host and parasite, transmission of that parasite to unrelated hosts may also be
less likely. Conversely, in Scenario 2 high host divergence may reduce prevalence as contacts between related individual hosts would
occur less often.
diversity might provide additional information on drivers
of parasite–host interactions (Ives & Helmus, 2010).
Phylo-β diversity approaches have led to major advances
in our understanding of the microbial, plant and animal
communities (e.g. Ley et al., 2006; Wang et al., 2013; Burbrink
et al., 2015; Herrera, 2016). For example, phylo-β diversity
of soil microbial communities can be highly deterministic,
with different habitats harbouring phylogenetically related
microbe communities, indicating strong environmental
selection of all microbe groups (Wang et al., 2013). Indeed,
obesity has been found to be associated with phylogenetically
distinct microbe communities (Ley et al., 2006). Phylo-β
diversity metrics are well developed in the non-parasite
literature (Bryant et al., 2008), but have rarely been used for
exploring host–parasite systems (Scordato & Kardish, 2014).
Model-based approaches such as PGLMMs (see Section
I.2f ) statistically model the likelihood of co-occurrence
as a function of species’ traits, environment, and their
interactions, while simultaneously accounting for and
measuring phylogenetic independence. By incorporating
phylogenetic distance into their fitting procedure, PGLMMs
can inform predictions for poorly studied (e.g. emerging)
diseases, assuming that data on close relatives are available.
PGLMMs have already been applied to interaction network
data (Rafferty & Ives, 2013), thus their use in MH–MP
systems would seem straightforward.
Biological Reviews 93 (2018) 950–970 © 2017 Cambridge Philosophical Society
To illustrate how one could apply these methods, we provide a hypothetical application of an eco-phylogenetic framework to understand multi-host/multi-pathogen dynamics at
a macroscale. For important zoonotic parasites, such as avian
influenza or rabies, there are large-scale molecular sequence
databases that can be exploited to help answer some of the
challenges proposed herein using eco-phylogenetic methods. For example, the Influenza Research Database (Zhang
et al., 2017) has hundreds of thousands of avian influenza
sequences from hundreds of subtypes from across the globe,
often from bird communities that are sampled intensively
in one location over a period of years. These data have
already been used to test and generate hypotheses about
the role of taxonomic α diversity in the formation of novel
influenza strains (Barton et al., 2014). Eco-phylogenetic models and metrics can expand this research further, and both α
and phylo-β diversity metrics and eco-phylogenetic models
can be used to predict diversity hotspots and to understand
the environmental and evolutionary dimensions of parasite
community coexistence.
Phylogenetic reconstruction of parasite sequence data (e.g.
using BEAST) can be used to create molecular operational
taxonomic units (OTUs) based on a model-derived threshold
value (e.g. Zhang et al., 2013). The geographic data attached
to each OTU can be used to estimate the environmental
niche, and the niche estimates can be overlaid to assess
Eco-phylogenetics and disease ecology
963
Fig. 7. Eco-phylogenetic and ecological niche modelling approaches can be used to understand co-infection patterns of parasite
genotypes at a macro-scale for two hypothetical bird species in South America. Data for this approach could be derived, for
example, from databases such as the Influenza Research Database (Zhang et al., 2017). In this case, the phylogeny (A) is constrained
by the ecological niche of the parasite genotypes (B) (i.e. niche conservatism). Coloured ovals in B represent the ecological niche
of parasites in environmental dimensions; those ecological niches are then projected onto geographic space to generate parasite
genotype geographic distributions (C). Dashed lines denote the distribution of each host bird. For the purposes of this example,
populations of bird species 1 were found to be co-infected by three parasite genotypes (a, b and c), and these genotypes represented
one clade in the phylogeny (A), indicating phylogenetic clustering, which might suggest that environmental filtering is important for
this co-infection pattern. Populations of species 2 were found to be infected by three genotypes (j, f & o), and in contrast to species
1, these genotypes were all from distinct clades (A), showing phylogenetic dispersion. This may indicate that biotic interactions or
possibly allopatric speciation history (Pigot & Etienne, 2015) are important for this co-infection scenario.
areas where there could be a high degree of overlap (Fig. 7).
To test if areas with high niche overlap are also diversity
hotspots, phylogenetic divergence can be calculated for the
community of OTUs at each location using the MPD
metric (weighted by OTU abundance at each location)
i,j δi,j
MPD = n(n−1) , i = j, where δ i,j is the pairwise distance
between OTU i and j and n is the number of OTUs. As
host sampling is likely to be uneven across locations (different
numbers of samples at each location), this metric, and all
other phylo-diversity metrics, is standardised by comparing
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Nicholas M. Fountain-Jones and others
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the observed community MPD with a value generated
from a null model generated by a randomisation method
(SESMPD ) (see Webb et al., 2002) to generate Standardized
Effect Sizes (SES): SES MPD = MPDobserved−mean(MPDnull)
. Positive
sd(MPDnull)
SESMPD values with high quantiles (≥ 0.95) is evidence for
phylogenetic dispersion, whereas negative SESMPD values
with low quantiles (≤ 0.05) is evidence for phylogenetic
clustering (Kembel et al., 2010). These values can be
compared against the degree of niche overlap and then
regressed against landscape or host variables (e.g. host
community MPD) to provide insights into drivers of these
patterns. Furthermore, they can provide landscape-level
insight into the mechanisms generating co-infection patterns
(Fig. 7).
Phylo-β diversity models can be used to make
inference about what drives OTU occurrence between
sites and can provide valuable insights into the degree to
which phylogenetically related OTUs co-occur. Phylogenetic
generalized linear mixed models (PGLMMs) (see Section I.2f) is
one method to do this and can be used to predict where
OTUs could occur (Ives & Helmus, 2011). PGLMMs can be
formulated in different ways, but one model that could be
used to untangle the phylogenetic and environmental drivers
of OTU coexistence through a series of logistic regressions
(Ives & Helmus, 2011) is:
Pr (Yi = 1) = μi
(1)
μi = logit−1 asp[i] + bsp[i] xsite[i] + csite[i]
(2)
bsp[i] = B + eslope[i] + gphylo[i]
(3)
2
eGaussian 0, σslope
In
(4)
2
sp
gGaussian 0, σphylo
(5)
2
cGaussian 0, σsite
In
(6)
where Y i is the presence (1) or absence (0) of the OTU, and
the probabilities μi are random effects with the distribution
logit(μi ), asp[i] is a categorical variable for each species and I
is an identity matrix. Logit(μi ) in this model is assumed to be
linearly dependent on x (an environmental or host variable)
through the coefficient bsp[i] . The covariance matrices e, g,
and c account for species-specific differences independent of
phylogeny, phylogenetic relatedness (assuming a Brownian
or some other model of evolution), and site-specific
differences in community structure, respectively (Ives &
Helmus, 2011).
The abiotic and biotic drivers of co-infection and parasite
community assembly can thus be investigated through a
combination of phylogenetic metric and model approaches,
Biological Reviews 93 (2018) 950–970 © 2017 Cambridge Philosophical Society
and MH–MP ecological niche modelling (Fig. 7 and Section
I.2d). Ecological niche modelling could identify where
co-infections could occur and phylo-diversity indices could be
used to explore the pattern of likely and realized co-infections
(Fig. 7). If the ecological niche dimensions of parasites are
governed by phylogenetic relatedness (Peterson et al., 1999),
phylogenetic placement of parasites may be important for
predicting parasite occurrence and persistence. For example,
if two subtypes of influenza have phylogenetically conserved
niches, they could be found in co-infections more often than
would be expected by chance. The phylogenetic structure
of coexisting parasites can also reveal insights into the
mechanisms underlying co-infection patterns. Significant
phylogenetic clustering of co-infecting parasites (or genotypes
of the same pathogen) in a host or population may be
evidence for environmental filtering (i.e. environmental
conditions restrictive to other species; Webb et al., 2002,
Fig. 7). Environmental filtering of parasites could be due
to external abiotic limits, or within-host immunological
responses filtering less-related pathogens. If co-infecting
parasites are phylogenetically dispersed (e.g. co-infection
with distantly related parasites), this could be evidence
for biotic interactions such as resource competition or
facilitation (e.g. Webb et al., 2002, Fig. 7). Such process-based
interpretations are, however, predicated on assumptions of
niche conservatism (see Section I), and other processes might
give rise to similar patterns (see Mayfield & Levine, 2010).
An emerging class of eco-phylogenetic methods are
exploring the possibility of quantifying the processes that
generate diversity (Pearse et al., 2015). These methods, while
still in their infancy, have the potential to help predict the
kinds of environmental conditions and ecological processes
that will lead to novel parasite diversity. For example,
the DAMOCLES approach places observed phylo-diversity
within the context of null hypotheses generated from
speciation and extinction patterns (Pigot & Etienne, 2015).
This approach may allow us to determine whether parasite
diversity is being created de novo within a system, or
whether it is moving from one local patch to another. Such
information has consequences for predicting the relevance
of vaccine development versus spatial isolation, and could be
powerful in linking short-term and longer-term management
interventions in systems where diseases are endemic.
VI. CHALLENGES
(1) Data
Data challenges represent one of the most limiting factors
in broad-scale application of eco-phylogenetic techniques to
disease ecology. Despite the decreasing cost of sequencing
and advances in technology, most wildlife disease studies
still rely on serological testing (i.e. testing for antibodies
for micro-parasites) or presence/absence or count data
(for macro-parasites) rather than parasite isolation and
genetic sequencing to investigate infection. Because current
Eco-phylogenetics and disease ecology
phylogenetic resolution is insufficient for most parasites,
there are few parasite groups where detection can be linked to
known phylogenetic relationships. Detection and sequencing
can be complicated, particularly for micro-parasites; viruses,
unlike bacteria and fungi, do not share a conserved
gene or genes across all members and thereby designing
specific probes can be difficult, especially for those taxa
where few sequence data are available. Furthermore,
identification of a specific pathogen can, for example,
rely on successful culturing which is variable among
lineages and requires significant laboratory protocols to be
developed (Mohiuddin & Schellhorn, 2015). Conventional
polymerase chain reaction (PCR) methods are also often
parasite specific, therefore co-infections may be missed
because they are not amplified (Shi et al., 2016; Simmonds
et al., 2017). This problem is compounded as parasite
diversity may be very large: for mammals alone there
are an estimated ∼320000 undiscovered viruses (Anthony
et al., 2013), > 11500 helminth species (Dobson et al.,
2008), and likely vast numbers of undiscovered bacterial
parasites. However, new molecular methods provide a
promising platform for identifying greater parasite diversity.
For example, advances in high-throughput metagenomic
techniques, such as environmental DNA (eDNA; Venter
et al., 2004) and virus-like particle (VLP) fraction (Melcher
et al., 2008; Mohiuddin & Schellhorn, 2015), can enable rapid
inventory of a broad range of parasites from within-host
and environmental samples (Shi et al., 2016; Simmonds
et al., 2017). Results from this approach can be used for
distinguishing OTUs based on a threshold of nucleotide
sequence identity and for phylogenetic reconstruction.
(2) Pattern, process, and functional characterisation
Obtaining suitable data is, however, just the first challenge. A
consideration only rarely dealt with by eco-phylogenetic studies is how to account for phylogenetic uncertainty. Most of the
methods discussed herein do not account explicitly for phylogenetic uncertainty, even though differences in tree topology
and branch lengths could impact measures of phylo-diversity
(Cadotte et al., 2010). Population genetic approaches, such
as BEAST, incorporate phylogenetic uncertainty directly
by sampling trees from a posterior distribution, and similar
strategies should be implemented for eco-phylogenetic methods. It is straightforward (if time-consuming), however, to
incorporate phylogenetic uncertainty if a posterior-predictive
approach is used, fitting models and calculating indices
across a set of plausible candidate phylogenies (in a Bayesian
setting, a posterior distribution of trees) and reporting
estimates across that set (Bollback, 2005).
How to link eco-phylogenetic patterns to processes
in infectious disease communities is a major conceptual
challenge. Community ecologists often infer the mechanisms
structuring a community based on observed patterns, but the
utility of doing so is fiercely debated (see Gotelli & McCabe,
2002; Gotelli & Ulrich, 2012; Connor, Collins & Simberloff,
2013). This is particularly the case within the field of
eco-phylogenetics where the processes that can be inferred
965
from phylogenetic clustering and dispersion have recently
been questioned based on modern coexistence theory (Mayfield & Levine, 2010; Gerhold et al., 2015; Kraft, Godoy &
Levine, 2015). Throughout this review, we have emphasised
the ways in which phylo-diversity metrics and models
can contribute to our understanding of eco-evolutionary
processes, moving beyond the use of phylogenetic structure
as a (controversial) proxy for ecological processes. However,
we stress that, within a public health perspective, any metric
or approach that can improve predictive power is valuable,
irrespective of its contributions towards the separate goal
of trying to understand the processes taking place. While
it is possible to identify general trends, the combination of
processes that shape any one parasite community are likely
to be unique due to the interaction with the host immune
system and host mobility and differences between parasite
types (e.g. macro- versus micro-parasites).
One way to disentangle process from pattern is to conduct
controlled manipulative trials, for example, co-infecting a
host or environment with either phylogenetically related
or unrelated parasites (or knocking out components of the
immune system) and then assessing changes in parasite
load and host health. Gilbert & Webb (2007) provide one
example, in this instance inoculating related host trees
with a matching suite of (fungal) parasites. Host–parasite
systems provide an ideal experimental system to assess
the phylogenetic patterns of community assembly, because
they represent rapidly evolving ‘island’ systems with diverse
assemblages that can be easily manipulated (Mouillot et al.,
2005). However, experiments can be unfeasible in some
systems due to ethical or practical considerations (i.e. most
parasite treatment methods are broad spectrum), but even if
imperfect, they can be informative (e.g. Ferrari et al., 2009;
Knowles et al., 2013; Pedersen & Antonovics, 2013). More
broadly, eco-phylogenetic data coupled with information
on functional traits provide another way to disentangle
process from pattern (Baraloto et al., 2012). For example,
morphological traits of the Dactylogyrus parasite species reaffirmed eco-phylogenetic trends indicating parasite clustering
(Mouillot et al., 2005). Functional traits can be defined as
morphological, biochemical, physiological, and phenological
characteristics of organisms that are linked to fitness (Violle
et al., 2007). However, as with phylogenetic data, functional
trait data for parasites are rare and mostly restricted to
macro-parasites (Morand, 1996; Mouillot et al., 2005). Developing techniques to record standardised functional trait data
for all parasites remains a fundamental challenge for parasite
community ecology, particularly for micro-parasites.
VII. FUTURE DIRECTIONS
We have illustrated how eco-phylogenetic methods can be
used to understand how host and parasite communities
shape infectious disease dynamics across scales. Such
information will be invaluable for making predictions
on how human activities might alter infectious disease
Biological Reviews 93 (2018) 950–970 © 2017 Cambridge Philosophical Society
966
dynamics. Anthropogenic forces are fundamentally altering
host–parasite systems (e.g. Gottdenker et al., 2014), and
the frequent movement of humans and animals across
the globe increases the possibility for hosts and parasites
to be introduced into new regions (Daszak et al., 2000).
The phylogenetic context of these introductions is likely
to be important in determining which parasites and hosts
successfully colonise. If an invader is distantly related to
native species, invasion success may be greater due to
less competition (e.g. Park & Potter, 2013), but predictions
regarding parasite spread are not straightforward. Virulence
might be lower because pathogens are less able to infect
the native host species, or virulence might be much
higher because native hosts are more susceptible (Lymbery
et al., 2014). Similarly, climate change and land use play
key roles in modifying the geographical distribution of
host and vector species (Altizer et al., 2013; Estrada-Peña
& de la Fuente García, 2014; Gottdenker et al., 2014),
shifting the phylogenetic structure of both host and
pathogen communities (Medeiros, Hamer & Ricklefs, 2013).
Therefore, it is important to understand how phylogenetic
community structure influences pathogen transmission, for
example, whether a shift towards more phylogenetically
clustered host communities will result in an increase in the
transmission of their associated infectious agents.
Human, domestic, and wild animal interactions are
becoming more frequent, and landscapes are becoming more
urbanised and fragmented, creating a greater potential for
parasite spill-over, spillback, and host switching (Suzán et al.,
2015). Mixing of parasites among common and novel hosts
may allow infectious diseases to persist in the environment
for longer periods, and may increase the chance of strains
recombining into new, potentially more virulent forms
(Engering, Hogerwerf & Slingenbergh, 2013). In addition,
to predict disease emergence better, there is a need to understand how parasites diversify and circulate in wild animal
populations and to evaluate how the control of one parasite
strain influences the prevalence and virulence of co-infecting
strains and parasites (Barclay et al., 2014). Eco-phylogenetic
techniques that start to untangle the processes that generate
diversity (such as DAMOCLES; Pigot & Etienne, 2015) are
likely to be particularly useful in this endeavour.
Eco-phylogenetic methods can play an important role
in enhancing wildlife and human health-surveillance
systems (Farrell, Berrang-Ford & Davies, 2013). Research
initiatives such as the Global Virome Project (http://www
.globalviromeproject.org/) will provide a large volume
of data that will improve our understanding about viral
phylogenetic relationships, and linking this information to
landscape and hosts over a variety of scales will enable a better
understanding of pathogen dynamics. For example, identifying landscape and host variables that correlate with parasite
phylo-diversity hotspots, where spill-over or co-infection
are likely, may help to identify susceptible populations,
contributing to the planning of risk-mitigation actions.
Moreover, identification of phylo-diversity hotspots can be
used to predict the probability of parasite translocation,
Biological Reviews 93 (2018) 950–970 © 2017 Cambridge Philosophical Society
Nicholas M. Fountain-Jones and others
establishment, and magnitude of health hazards, essential
for risk-based analysis and resource prioritisation.
VIII. CONCLUSIONS
(1) Eco-phylogenetic approaches have revolutionised
our understanding of free-living communities, yet formal
application of this framework to disease ecology and
epidemiology has lagged behind. Studies that have applied
eco-phylogenetic techniques to understand host–parasite
systems have led to advances in our understanding of
pathogen spill-over and the evolutionary relationships
between parasites and hosts in particular.
(2) An eco-phylogenetic framework coupled with Bayesian
phylodynamics and ecological niche modelling can help
answer questions central to disease ecology from within-host
to landscape scales. This unified framework can help us
to understand the antagonistic and synergistic relationships
between hosts and parasites that may enable better prediction
of disease threats.
(3) We suggest that experimental studies coupled with
increased parasite genomic sampling within individuals and
among populations and communities will help develop the
conceptual foundations of this nascent field. Techniques
that help characterise functional traits across parasites will
assist in understanding the processes underpinning complex
relationships between hosts and parasites, particularly in a
world with increased anthropogenic change.
(4) As our knowledge of global diversity improves,
and in particular with the expansion of the multi-host
multi-pathogen framework, eco-phylogenetic methods are
likely to become an increasingly important component of
infectious disease research.
IX. ACKNOWLEDGEMENTS
We thank the Minnesota Institute on the Environment
Mini-Grant that sponsored the workshop that formed the
basis of this paper. N. M. F. -J. and M. E. C. were funded by
National Science Foundation (DEB-1413925); M. E. C. was
funded by the University of Minnesota’s Office of the Vice
President for Research and Academic Health Center Seed
Grant. K.V. was funded by NSF grant 1619072 Ecology and
Evolution of Infectious Diseases program.
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(Received 4 May 2017; revised 22 September 2017; accepted 28 September 2017; published online 8 November 2017)
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