Introduction.tex

\section{Pathogen richness and the impacts of zoonotic diseases}

\tmpsection{General intro}


\tmpsection{This diversity poses a zoonotic risk} %significance

Over 60\% of newly emerged diseases are zoonotic (acquired from animals) with wild animals being the predominant source \cite{jones2008global, woolhouse2006host, taylor2001risk}.
Zoonotic diseases can be extremely virulent with viruses such as Nipah  and Ebola having case fatality rates over 50\% \cite{luby2009recurrent, lefebvre2014case}.
Furthermore these pathogens can have large economic costs.
For example, SARS is estimated to have cost \$40 billion \cite{knobler2004learning}.
In particular these impacts can have huge effects on lower-income economies.
For example, both Liberia and Guinea experienced negative per capita growth rates of -2\% due to the Ebola epidemic in 2014 \cite{ebolaWorldbank, ebola2015worldbank}.
More generally, death rates per 1,000 people living with AIDS are up to ten times higher in developing countries than in Europe and North America \cite{granich2015trends}.
The global richness of pathogens is large but mostly unknown \cite{poulin2014parasite}.
Recent studies suggest that the global number of mammalian virus species is of the order of hundreds of thousands \cite{anthony2013strategy} while only 3,000 virus species, across all taxonomic groups, are currently described \cite{ICTV}.
This large pool of unknown pathogens presents a continuing risk of new pathogens spilling over into humans.


\tmpsection{Pathogen diversity has an important role in zoonotic diseases and we can optimise surveillance if we understand it}

Surveillance of zoonotic diseases is crucial for reducing the health impacts of these diseases.
In particular it is important to categorise and describe diseases before they spill over into humans.
For example, SARS was not identified until months into the pandemic \cite{drosten2003identification}. 
It is also important to improve our ability to predict when outbreaks will occur. 
For example, if it is known that there is \textit{i})~a higher disease prevalence in a given host species than normal, or \textit{ii})~a greater-than-usual abundance of a species that is a known reservoir of a high risk zoonotic disease, or \textit{iii})~increased contacts between humans and a pathogen reservoir, preparations can be made for a potential outbreak in that area.

However, funds for zoonotic disease surveillance are limited and so efforts must be optimised.
A number of different metrics could be used to optimise zoonotice disease surveillance.
One broad group of measures includes factors that predict the chance of transmission of a disease from wildlife hosts to humans.
These measures include bushmeat usage and overlap between host and human population density \cite{brierley2016quantifying, estrada2014effects, redding2016environmental}.
Alternatively, knowing which species are likely to have many pathogens allows us to sample and identify potentially zoonotic viruses efficiently.
In practice, surveillance effort should be prioritised using a combination of these factors.
In particular, one sensible approach would be to spatially prioritise surveys using factors that affect risk of transmission \cite{brierley2016quantifying,jones2008global} and then prioritise host species within an area by species specific factors that affect risk of transmission and factors that are likely to predict host pathogen richness \cite{kamiya2014determines, luis2013comparison, nunn2003comparative}.

Suggested factors that might control pathogen richness include individual, environmental and population level traits.
Individual traits that have been studied include body mass and longevity.
Increased body mass is expected to increase pathogen richness as large bodies provide more resource for pathogens to consume and potentially more niches for them to occupy \cite{kamiya2014determines, arneberg2002host, poulin1995phylogeny, gomez2015diversity, bordes2008bat}.
Increased longevity is also expected to increase pathogen richness by increasing the number of pathogens a host encounters in its lifetime \cite{nunn2003comparative, ezenwa2006host, luis2013comparison}. 
Environmental factors may also play a role. 
Latitude has been studied as a proxy for environmental factors \cite{poulin2010latitudinal, kamiya2014determines}.
It is predicted that warmer climates promote species richness via metabolic mechanisms or by increasing the rate of evolution \cite{brown2004toward, dunn2010global, rohde1992latitudinal}.
Furthermore, population level traits that affect the dynamics of disease spread have also been studied.
Animal density \cite{kamiya2014determines, nunn2003comparative, arneberg2002host} and sociality \cite{bordes2007rodent, vitone2004body, altizer2003social, ezenwa2006host} have both been predicted to increase pathogen richness by increasing the rate of spread of new pathogens.
Population structure is difficult to study directly, but genetic measurements of population structure and measures based on the geographic shape of the species range have been used to study population structure \cite{nunes2006localized, maganga2014bat, gay2014parasite, turmelle2009correlates}.
However, the results thus far have been contradictory with studies finding both positive and negative relationships between pathogen richness and populations structure.
Finally, species with larger range sizes are expected to have higher pathogen richness as they experience a wider range of environments and have more sympatric host species \cite{kamiya2014determines, nunn2003comparative}.
These relationships can provide a basis for predicting which species will have high pathogen richness and should be prioritised for sampling and surveillance.


However, without a better mechanistic understanding of how pathogen richness is created and maintained it is difficult to predict how pathogen richness, and therefore zoonotic disease risk, will respond to global change.
Competition between pathogens can occur by different mechanisms: immunological mechanisms such as cross-immunity or shared immune response \cite{fenton2010applying} and ecological mechanisms such as removal of susceptible hosts by death \cite{rohani2003ecological} or competition for internal host resources \cite{griffiths2014analysis}.
As in ecological systems, competition leads us to the expectation that competitive exclusion occurs \cite{bremermann1989competitive, martcheva2013competitive, ackleh2003competitive, ackleh2014robust, turner2002impact}.
Therefore, the large number of coexisting parasite species needs an explanation.



\section{Influence of population size and structure on pathogen richness}


\tmpsection{Theoretical evidence that structure and density increase richness}

\subsection{Single-pathogen models}
% Structure + single path theory

In an unstructured, or well-mixed, population, epidemiological interactions --- events in which pathogens can potentially be transmitted --- occur randomly between individuals; an individual is equally likely to interact with any other individual.
In contrast, in a structured population, epidemiological interactions are not random.
This non-randomness can arise for many different reasons.
For example, the geographic distribution of individuals or social groups can both cause individuals to be more likely to interact with certain individuals than others \cite{keeling1999effects, vespignani2008reaction, may1983epidemiology}. 
Furthermore, different processes can structure a population depending on how pathogens are transmitted.
In the case of a sexually transmitted disease, epidemiological interactions only occur between mating individuals and the mating systems of the species will govern which individuals interact \cite{castillo1995dynamics, castillo1996competitive, eames2002modeling}.
In contrast, if a pathogen is transmitted through the environment, by persisting in a water source for example, epidemiological interactions can occur between individuals that are never in close proximity to each other \cite{cross2009wildlife, park2012infectious}.
In these different cases, the processes by which population structure forms are different. %todo refs.

There is a large literature on the role of population structure on single-disease dynamics, as reviewed by \textcite{pastor2015epidemic}, driven by applications to human health as well as computer viruses \cite{pastor2001epidemic} and the social spread of information \cite{goffman1964generalization}.
In particular, work has concentrated on how population structure affects the basic reproduction number, $R_0$ \cite{colizza2007invasion, barthelemy2010fluctuation, wu2013threshold, may2001infection, pastor2001epidemic}.
Formally, $R_0$ is the average number of individuals infected by a single infected individual entering a na\"ive population where no individuals have previously acquired immunity to the pathogen of interest.
More intuitively, $R_0$ combines relevant parameters to yield a threshold above which a disease is expected to infect a significant proportion of the population \cite{may1979population, anderson1979population}.
Below the threshold, only small outbreaks that quickly die out are expected.
% Density + single path theory

The roles of population size and density in the dynamics of single pathogens are also well established \cite{may1979population, anderson1979population, heesterbeek2002brief, lloyd2005should}.
Broadly, larger populations can maintain a disease more easily by having a larger pool of susceptible individuals (individuals without acquired immunity) and having a greater number of new susceptible individuals enter the population by birth or immigration \cite{may1979population, anderson1979population}.
High density populations are expected to have a greater number of contacts between individuals and so promote the spread of a pathogen.
However, there is much discussion about if, and when, the number of contacts might scale independently of density \cite{mccallum2001should}.

\subsection{Multi-pathogen models}
% multi path is important
While the majority of theoretical work considers single pathogens, with models examining whether a pathogen can spread and persist in a population, much less work has been done on multiple pathogen systems.
Studies have found tens \cite{anthony2013strategy} or even hundreds \cite{anthony2015non} of virus species in a single host species.
%This suggests that the global number of mammalian virus species is of the order of hundreds of thousands \cite{anthony2013strategy} while recent large databases include nearly 2,000 pathogens from approximately 400 wild animal hosts \cite{wardeh2015database}.
Therefore ignoring inter-pathogen competition is an oversimplification.

% structure and multi theory
A number of studies have considered the case where two pathogens spread concurrently and examine which pathogen infects more individuals.
These studies have found that increased population structure reduces dominance of the more competitive strain \cite{van2014domination, poletto2013host, poletto2015characterising}.
However, this again reveals little about how pathogen communities form and what factors control total pathogen richness.
Far fewer papers explicitly study long term coexistence of two or more pathogens.
Those that do commonly find that competitive exclusion is likely \cite{castillo1995dynamics, bremermann1989competitive, martcheva2013competitive, ackleh2003competitive, ackleh2014robust, turner2002impact}.
Mechanisms that have been shown to allow pathogen coexistence include superinfection \cite{may1994superinfection, li2010age}, density-dependent deaths \cite{ackleh2003competitive, kirupaharan2004coexistence} and differing transmission routes \cite{allen2003dynamics}.

% density + multi theory
The specific role of density on the ability of pathogens to coexist has not been theoretically studied though it is commonly found to promote pathogen richness in comparative empirical studies \cite{kamiya2014determines, nunn2003comparative, arneberg2002host}.
The few papers that have directly studied how coexistence of pathogens responds to population structure have found that population structure can allow pathogens to coexist even though competitive exclusion would occur in a fully mixed population \cite{qiu2013vector, allen2004sis, nunes2006localized}.
Furthermore, genetic diversity has been shown to be maximised at intermediate levels of population structure \cite{campos2006pathogen}.
The roles of population structure and social group size have been examined in comparative studies \cite{maganga2014bat, gay2014parasite, turmelle2009correlates, altizer2003social, bordes2007rodent, ezenwa2006host, rifkin2012animals, vitone2004body}.
There is much disagreement between these studies.
Population structure has been shown to promote \cite{maganga2014bat, turmelle2009correlates} and inhibit pathogen richness \cite{gay2014parasite}.
Similarly, group size has been shown to promote \cite{rifkin2012animals, bordes2007rodent} and inhibit \cite{ezenwa2006host} pathogen richness.
While increased group size should generally decrease population structure, the literature is rarely clear on the relationships between these and other variables.



\tmpsection{Specific intro}

\section{Bats as reservoirs of zoonotic diseases}

\tmpsection{Bats are a particular culprit of this risk, and have unknown diversity and interesting population structure.} %context

In recent decades bats have been implicated in a number of high profile zoonotic outbreaks such as Nipah \cite{field2001natural, halpin2011pteropid}, Ebola \cite{leroy2005fruit}, SARS \cite{li2005bats} and Hendra \cite{field2001natural}.
These outbreaks have led to much research on whether bats are a particularly important source of zoonotic disease \cite{luis2013comparison, olival2015bats, wang2011mass} and examinations of factors, such as flight, social living and longevity, that might predispose them to being reservoirs of zoonotic viruses \cite{calisher2006bats, o2014bat, dobson2005links, racey2015uniqueness, kuzmin2011bats}.
Given that bats are the second largest order of mammals \cite{wilson2005mammal}, we may expect them to be the source of many viruses simply through weight of numbers \cite{luis2013comparison}.
The broad conclusions are that while bats do host more zoonotic virus species than other groups \cite{luis2013comparison} they do not host more virus species per host species \cite{olival2015bats}.


Many factors of bat populations make them epidemiologically interesting.
They have highly varied and sometimes complex social structures \cite{kerth2008causes}.
Some species are largely solitary or live in very small groups --- e.g., \emph{Lasiurus borealis} \cite{shump1982lasiurus} --- while other species live in colonies of millions of individuals --- e.g., \emph{Pteropus scapulatus} \cite{birt2008little}.
These groups can be very stable \cite{kerth2011bats, mccracken1981social}.
Further complexity arises due to their propensity for seasonal migration \cite{fleming2003ecology, richter2008first, cryan2014continental} and seasonally changing social organisation such as maternity roosts, hibernation roosts and swarming sites \cite{kerth2008causes}.
Finally, their ability to fly means that populations can be well mixed across large distances \cite{peel2013continent, petit1999male}, though this is highly variable with some species having limited dispersal \cite{wilmer1994extreme}.

However, the population density of many bat species, particularly tree roosting species, is unknown \cite{clement2013estimating}.
As they are small, nocturnal and difficult to identify in flight, estimating their density is incredibly difficult without disruptive and time-consuming roost surveys \cite{kloepper2016estimating, humphrey1971photographic, sabol1995technique}.
Furthermore, bat densities are generally estimated by counting bats in roosts and dividing this number by area which assumes all roosts have been surveyed \cite{speakman1991minimum, zahn2006population, moreno2004colony}. 
As density is associated with pathogen richness \cite{kamiya2014determines} and central to epidemiological models \cite{may1979population, anderson1979population} this leaves large gaps in our understanding of disease processes in this taxon.

\tmpsection{Bats are hard to detect etc.}




\section{Thesis overview}

In this thesis I examined the role of population structure and density on pathogen richness.
I used bats as a case study throughout due to their interesting social structure and importance as zoonotic	reservoirs.
I combined empirical, comparative studies with simulation models.
This allowed me to study specific mechanisms while linking my theoretical insights to real-world, empirical tests of hypotheses.


\tmpsection{Chapter 2}

First, in Chapter~\ref{ch:empirical}, I empirically tested the hypothesis that population structure is associated with pathogen richness (measured as known viral richness) in wild bat populations.
To ensure robust results I used two measures of population structure --- the number of subspecies and gene flow --- and a larger data set than previous studies.
For both measures I found that bat species with more structured populations have more known viruses.
This relationship is still present after controlling for study bias and phylogenetic nonindependence.
I also tested for relationships between body mass and pathogen richness, and range size and pathogen richness, and found strong support for larger bodied bats carrying more viruses and mixed support for range size promoting pathogen richness.

\tmpsection{Chapter 3}

In Chapter~\ref{ch:sims1}, I examined one specific mechanism by which population structure may promote increased pathogen richness.
I tested whether increased population structure can allow newly evolved pathogen strains to invade and persist more easily.
I modelled bat populations as individual-based, stochastic metapopulations and examined the competition dynamics of two identical pathogen strains.
I tested two factors related to host population structure: dispersal rate and the number of links between colonies.
I found that increased dispersal rate significantly increased the probability of a newly evolved pathogen invading and persisting in the population.
However, this was only the case at intermediate transmission rates.
I did not find a significant difference in invasion probability due to the number of links between colonies.

\tmpsection{Chapter 4}

Next, I examined the relationships between a number of elements of population structure (Chapter~\ref{ch:sims2}).
I clarified the interdependence between range size, population size and density.
I also noted that population size can be decomposed into colony size and the number of colonies.
Using the same model as in Chapter~\ref{ch:sims1}, I then tested which of these factors are most important in promoting pathogen richness.
Specifically I tested which factor most strongly promotes the invasion and establishment of newly evolved pathogens.
I found that population size is more important than population density and that colony size is the important component of population size.

\tmpsection{Chapter 5}

Given the importance of host population size and density on pathogen richness it is important to have good population estimates for wild bat populations.
However, there are currently very few measurements of bat population size due to their small size, nocturnal habit and difficulties in identification.
Therefore I aimed to develop a method for estimating bat population size from acoustic data, specifically data collected by the iBats project \cite{jones2011indicator}.
In Chapter~\ref{ch:grem} I developed a generally applicable method --- based on random encounter models \cite{rowcliffe2008estimating, yapp1956theory} --- for estimating population sizes of animal populations using camera traps or acoustic detectors.
I used spatial simulations to test the method for biases and to assess its precision.
I found that the method is unbiased and precise as long as a reasonable amount of data is collected.


\tmpsection{Chapter 6: Conclusions}

%to do Conclusions chapter
Finally, in Chapter~\ref{ch:discussion}, I discuss broader conclusions, applications and implications of my results.
I also discuss potential future directions for research.