Towards A More Healthy Conservation Paradigm: Leveraging Disease Ecology To Aid Biological Conservation

A brief history of disease ecology

Parasites remain the most ubiquitous, but possibly least understood, members of global ecosystems. Ranging from microscopic viruses to tapeworms measuring over 10 meters, parasites (Box 1) and the diseases they cause have long occupied an important place in the social and scientific domains. However, despite our long fascination with parasites, we are only now beginning to understand how deeply they are embedded within ecological systems. This is especially important in present times when human-mediated alterations in the environment – from global climate change to local habitat fragmentation – are increasingly impacting the structure and function of natural ecosystems (##REF).

Such anthropogenic alterations can interact in complex ways to directly or indirectly impact disease dynamics in human and wildlife populations (Fig. 1). Critically, these factors combine to create a “geographic arena of pathogen emergence” (Hoberg and Brooks 2015) that has been likened to an “evolutionary minefield of potential emerging diseases” (Brooks and Ferrao 2005). Emerging and re-emerging diseases have been characterized as one of the greatest challenges of our times, and it is increasingly becoming clear that managing parasites in natural populations critically depends upon viewing disease as an eco-evolutionary process.

Disease ecology is an interdisciplinary area of research, which primarily focuses on “the ecological study of host–pathogen interactions within the context of their environment and evolution” (Kilpatrick 2010).

The field has been growing rapidly since the early 1990s (Real, 1996), strengthening our understanding of how ecology is important for our understanding of disease dynamics, while highlighting how the study of disease could improve our understanding of ecology (Johnson et al.

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2015a). Indeed, over the last three decades there has been a growing realization that parasites have an important role in affecting eco-evolutionary processes at the individual-, population- and ecosystem scales (Johnson et al. 2015a).

While the effects of parasites on individual health has long been recognized and it remains an important cornerstone of human and veterinary medicine, the role of parasites in regulating host populations (e.g., abundance or geographic distribution) has not always been accepted. Early viewpoints on pathogen virulence maintained that “successful” or “well adapted” parasites, would not to be able to regulate host populations because evolution would drive a reduction in harm to their hosts (Holmes 1982). This viewpoint was primarily based on the logic that parasites that extirpate their hosts would be evolutionary dead ends. However, this viewpoint was fundamentally challenged by the pioneering theoretical models of Anderson and May (1978, 1979, 1981, 1982).

These modelsrevealed that a wide variety of evolutionary outcomes are plausible during host-parasite coevolution.From a parasite’s perspective, an evolutionarily stable strategy of virulence would depend on how the relative extent of host damage (i.e., pathogenesis) trades off against parasite transmission rate (De Roode et al. 2008). It is now well recognized that myriad factors can impact the strength of this trade-off, including transmission route, co-infections and evolutionary history (Lipsitch et al. 1996; Alizon 2013, Cressler 2016, Bose 2016). Consequently, virulence cannot be characterized as a fixed property of either parasite or host, but rather as an outcome associated with a specific host-parasite interaction, an interaction that is in turn embedded within a broader eco-evoloutionary context (Methot and Alizon 2014).

Public health interventions for infectious diseases (e.g. vaccines) have been very successful in achieving population-level reductions in disease prevalence, though sometimes with an unanticipated cost of increased disease severity (Box 1). In natural populations, extremes of parasite virulence could develop under specific conditions. For example, theoretical models reveal that the evolution of reduced virulence would be expected in small host populations because in such populations the spread of a highly transmissible pathogen becomes self-limiting as it uses up limited local host resource (Boots and Melor 2007). Alternatively, extremely virulent parasites are often associated with recently emerged diseases or those introduced into naïve host populations (Suicide King dynamics; ##REF). In general, the most common view characterizes host-parasite co-evolutionary dynamics as an “arms race”.

Thus, virulent parasite strains are expected to select for the evolution of novel mechanisms to fight the infection in their hosts, thus leading to negative frequency-dependent selection (and thus temporal cycling) of host and parasite genotypes (Red Queen dynamics; ##REF). From this perspective of antagonistic coevolution between hosts and parasites, the only way a host can improve its fitness in the face of parasite attack is by negatively impacting the parasite’s fitness by either avoiding infection (e.g., through behavioral mechanisms; ##REF) or by reducing parasite load once infected (e.g., immune-mediated killing; ##REF). However, recently, a growing body of theoretical and empirical data suggests that reduced parasite virulence may not be necessarily driven by (host-mediated) reduction in parasite fitness but rather associated with increased tolerance (i.e., mechanisms that reduce parasite-mediated fitness costs at a given parasite burden).

By reducing parasite-mediated reduction in host fitness without directly killing parasites, tolerance can concomitantly increase the fitness of the infected host and its parasites (##REF). Contrary to resistance (and indeed current disease management interventions), tolerance is expected to increase disease prevalence, and forces us to reevaluate many fundamental concepts related to disease management (##REF). For example, high tolerance makes parasite prevalence data a poor indicator of true disease burden, or alternatively when estimation of disease burden is primarily based on clinical symptomatology (as in many human diseases) and the importance of asymptomatic carriers may be underestimated.

Evolutionary theory predicts that tolerance is more likely to be selected as compared to resistance because tolerant individuals (unlike resistant ones) help increase the prevalence of the very parasites that their offspring have effective defense mechanisms to counteract (thus increasing the relative fitness of their offspring in relation to other individuals in the population). Additionally, hosts may be more likely to invest in tolerance vs. resistance if resistance mechanisms are non-specific (thus reducing the potential for antagonistic co-evolution with the parasite) and/or expensive (in terms of maintenance or activation costs).

Classical examples of successful disease control primarily showcase host-specific infections that generally produce long-lived immunity (e.g., measles and smallpox; ##REF). However, most diseases in natural populations, including recently emerging or reemerging disease, are more complex (Lloyd-Smith 2009), and include the involvement of multiple host and parasite species, as well as the broader ecological community (including non-host species) that can alter host–parasite interactions, and thus disease dynamics (Johnson et al., 2015). This perspective focuses specifically on the role that parasites play in biological conservation. While we summarize a large body of literature relating to disease ecology, this perspective is not meant to be an exhaustive literature review (several excellent recent, and not so recent, reviews include: ##REF).

The first section focuses on the most obvious implications of parasites from a conservation perspective: disease-mediated extinctions and wildlife epidemics. The next two sections take a step back and focus on how elucidating host-parasite interactions has improved our understanding of the ecological and evolutionary dynamics affecting hosts at the individual, population and community or ecosystem scales. The final two sections primarily focus on the meaning of “health”, at the individual and population levels, as well as that of entire ecosystems. We believe that the role of parasites as drivers and/or indicators of ecosystem health is an especially exciting area of research that has the potential to fundamentally alter our view of parasites and their role in biological conservation.

Disease-mediated extinctions and epidemics in wildlife

There is an increasing recognition that wildlife disease has important implications for biological conservation through their effects on biological diversity. There have been several examples of large-scale epidemics in natural populations that have been directly implicated in loss of biological diversity at local or global scales. Among these epidemics, some of the best known examples include: (1) Avian malaria: Avian haemosporidians (Apicomplexa: Haemosporida; Plasmodium and other related genera such as Haemoproteus and Leucocytozoon) are a globally distributed group of vector-borne blood parasites that infect a wide array of bird taxa (Valkiŭnas 2005).

Avian malaria caused by Plasmodium spp. is one of the most important emerging infectious diseases of wild bird populations globally (Tompkins and Gleeson 2006, Beadell 2007, Schoener et al. 2014, Marzal et al. 2015), and large-scale mortalities in native wild birds have been well documented due to accidental introduction of Plasmodium spp. and the vector Culex quinquefaciatus into island bird communities which had no co-evolutionary history with these blood-borne parasites (e.g., Hawaii and New Zealand; Atkinson et al. 2000; Howe et al. 2012; Lapointe et al. 2012); (2) Chytridiomycosis: Caused by the fungus Batrachochytrium dendrobatidis, is one of the most important emerging infectious diseases of amphibians, and has been linked to population declines, local extirpations and even extinctions globally (Pounds 2006; Skerratt et al. 2007). Initial reports indicated that the fungus likely originated in Africa (Weldon 2004), but this remains unresolved despite more recent genome-wide data (Rosenblum 2013).

More recently, a newly discovered sister pathogen, B. salamandrivorans (Martel 2013), has been linked to the population declines of salamanders (Martel 2014); (3) White-nose syndrome: caused by the fungus Pseudogymnoascus destructans, a highly pathogenic fungus that colonizes the skin of bats (Blehert 2009). The condition is named for the distinctive fungal growth on the muzzles of hibernating bats. The pathogen was first identified in 2006 from New York (Blehert 2009), and to date has been responsible for the deaths of millions of bats in North America (USFS 2012), and has led to local extirpations of many bats species, both endangered (e.g., Indiana bat; Myotis sodalist; Reed 2013) and formerly common ones (e.g., little brown bat, Myotis lucifugus; Frick et al. 2010).

Apart from the above mentioned diseases, a number of other pathogens have been implicated in populations declines including the rinderpest virus epidemic in wild ruminants in Kenya (Kock 1999), Ebola virus epidemic in Zaire that was responsible for the deaths of over 5000 gorillas (Barmejo 2006), large-scale declines of North American bird populations due to West Nile virus (LaDeau 2007). However, the number of extinctions that can unequivocally be attributed to infectious disease is relatively small (deCastro 2005; McCallum 2012;), and notable examples include: extinction of the Australian frog, Taudactylus acutirostris, due to B. dendrobatidis (Schloegel et al., 2006), and the extinction of two species of rats (Rattus macleari and R. nativitatis) on the Christmas Islands due to Trypanosoma lewisi (Wyatt et al., 2008). Indeed, epidemiological models generally predict that host-specific pathogens are unlikely to cause extinction because transmission rates tend to decrease as host density decreases (McCallum and Dobson, 1995). Indeed, the requirement of a “threshold size” of susceptible individuals required to maintain a disease in a population forms the basis of disease eradication through vaccination (##REF).

There are broadly two major mechanisms that could allow disease-mediated extinctions. First, when a parasite is a generalist, capable of infecting more than one species, it can persist in a reservoir host (i.e., one that is relatively unaffected by the pathogen; deCastro 2005). A meta-analysis of parasite-mediated extinction risk has revealed that parasites that affect host threat status were predominantly viruses and bacteria that infect a wide range of host species, including domesticated animals (Pederson 2007). Indeed, most of the pathogens cited above (i.e., B. dendrobatidis. P. relictum, T. lewisi and the Rinderpest virus) are generalist parasites infecting a broad diversity of hosts. Additionally, specific parasite strains may be more successful invaders into novel host communities as compared to others.

For example, analysis of P. relictum invasion into New Zealand found that parasite lineages that successfully invaded are more globally generalist (both geographically widespread and with a broad taxonomic range of hosts) than those not co-introduced to New Zealand (Ewen 2012). The second mechanism that can lead to disease-mediated extinctions is frequency-dependent transmission (i.e., when transmission rate depends on frequency of host contact, rather than host density). Classical examples of frequency dependent transmission include vector-borne diseases (e.g., West nile virus and avian malaria), wherein high transmission rates can be maintained even at low host populations when the vector is efficient at finding their target species (Boots & Sasaki 2003). Other disease with frequency-dependent transmission include sexually or socially transmitted diseases. For example Devil Facial Tumor Disease is a disease with frequency-dependent transmission which has caused severe declines in Tasmanian Devil populations because disease transmission is unlikely to die out even when devil densities are extremely low (McCallum, 2008).

Genetic (co)structuring of hosts and parasites

Landscape genetics and genomics utilize molecular tools in conjunction with population genetic and/or phylogeographic analyses to elucidate ecological and evolutionary processes at multiple scales of biological organization (Manel & Holderegger 2013; Petrel 2013). The integration of such genetic methods with epidemiological information (e.g., parasite prevalence) has provided critical insights into disease transmission dynamics and epidemiological history of emerging infections (Archie et al. 2009). Additionally, analyses of epidemiological patterns and/or parasite genetics can provide novel insights into animal social dynamics, spatial movement and interspecific interactions (see below).

Social structure: Spatial aggregation of related individuals, typically exhibiting high contact rates, is a common form of social organization in wildlife (Altizer et al. 2003). Social groups size is generally driven by levels of competition for critical resources such as food (e.g., in jackals, ##SPP; MacDonald 1979) or mates (e.g., in the striped mouse, Rhabdomys pumilio; Schradin 2012). However, there is generally a positive association between group size and parasite infection risk (Griffin and Nunn 2012), and thus elevated risk of parasitism could be an important cost associated with sociality. For example, gorillas (##SPP) in Central Africa that clustered around seasonally fruiting trees were more affected by the outbreak of Ebola (97% mortality) compared to solitary individuals (77% mortality) (Caillaud et al. 2006).

Aggregation of resources, such as supplemental feeding, can increase infectious disease transmission risk (Sorenson 2013; Brook 2012), and the clustering of birds around bird feeders during winter have been linked to mycoplasmal conjunctivitis outbreaks in North America (Dhondt et al. 2005). At the population level, parasite transmission rates are expected to be higher in populations with low levels of social and/or spatial structure (i.e., smaller group sizes and more movements between groups; Craft 2015). For example, in the case of lions (##SPP), infection risk in animals living in prides is epidemiologically similar to homogeneous populations up to 20 per cent larger (Caillaud 2013). Generally, we would expect increased levels of social interactions between related vs. non-related individuals, and several studies have confirmed this pattern using epidemiological data.

For instance, higher contact rates within family groups has been demonstrated in white-tailed deer (Odocoileus virginianus) because bovine tuberculosis (a parasite transmitted primarily by direct contact) infected deer are more closely related than non-infected deer (Blanchong et al. 2007). Similar findings have been reported in the case of chronic wasting disease in deer (Cullingham 2011a ; Magle et al. 2013). However, the effects of host relatedness on disease risk, will depend upon the spatial scale relevant for parasite transmission. For example, in raccoons (Procyon lotor) infection risk associated with a directly transmitted parasite (canine distemper) was positively related to contact rates (i.e., familial structure) within spatially discrete habitat patches, while infection risk associated with an environmentally transmitted parasite (Leptospira spp.) was positively related to contact rates (i.e., geneflow) amongst the patches (Dharmarajan et al., 2012).

While contact networks are increasingly are being used to study parasite transmission dynamics in wildlife (White 2017), pathogen networks can be used to provide critical information on social association patterns in natural populations. For example, bacterial transmission networks are a powerful tool that have been used to elucidate social structure in wild lizard and giraffe populations (vanderWaal 2014; Bull 2012). Interestingly, bacterial networks are not associated with social structure in elephants, but rather seem to be driven primarily by habitat utilization patterns or individual host characteristics (i.e., sex and age) (Chiyo 2014).

Geographic structure: The spatial scale of genetic correlation among individual hosts or demographic groups can reflect the amount of gene flow that occurs (##REF). Thus, genetic data can aid biological conservation by providing information on the functional connectivity among parasite, host and vector populations in a landscape. Levels of gene flow can help characterize the spatial spread of phenotypes of epidemiological importance (e.g., virulence, drug and pesticide resistance) in both parasite and vector populations, and can also help quantify parasite invasion risk (Shwabl 2017). Landscape genetics has been an especially powerful approach that has been used to effectively identify landscape features influencing the spatial spread of parasites, including chronic wasting disease prions in deer (Blanchong et al. 2008, Cullingham et al. 2011a, Lang and Blanchong 2012, Robinson et al. 2012b, Robinson et al. 2013, Kelly et al. 2014) and the rabies virus in mesocarnivores like raccoons and skunks, Mephitis mephitis (Biek et al. 2007; Cullingham et al. 2009, Cote 2011; Paquette et al. 2014),.

However, landscape features do not necessarily limit animal movement in all species. For example, DeYoung et al. (2009), for example, were unable to identify natural boundaries to gene flow, and thus rabies spread, for gray foxes (Urocyon cinereoargenteus). Landscape genetics approaches also form a powerful approach to quantify dispersal resistance/permeability surfaces for natural populations. For example, Bouyer (2015) used microsatellite data to develop landscape resistance surfaces and thus identify candidate populations for Tsetse fly (##SPP) elimination. Alternatively, the genetic structure of parasite populations can be used to elucidate characteristics of the host population that could not be obtained from host genetics alone (Dharmarajan et al., 2016). In the case of migratory birds, host genetics is a poor indicator of migratory routes because genetic recombination only occurs in the breeding (summer) but not nonbreeding (winter) grounds.

However, critical insights into migratory strategies can be obtained through the genomic characterization of parasites, such as avian influenza virus in waterfowl (Lam et al. 2012; Hill et al. 2012b; Newman 2012). For example, the genetic analysis of avian influenza in mallards (Anas platyrhynchos) in California wintering grounds revealed that the migrant birds carried variants from their northern breeding grounds in Alaska and the NW Pacific Rim (Hill et al. 2012a). Using phylogenetic approaches to analyze high-resolution pathogen genotype data can help detect and time historical changes in epidemiological patterns (Stadler 2013), which can have implications for conservation. For example, genome resequencing of the chytrid fungus, shows that the evolutionary history of this pathogen predates its continent-scale movement and associated disease outbreaks (Rosenblum 2013). Indeed, chytrid fungus phylogenetic analyses identified a single pathogen lineage consistent with a unique geographic origin followed by a rapid spread (James et al. 2009), a pattern likely driven by the commercial movement of frogs (Fisher et al. 2012).

Similarly, sequencing of the hemagglutinin gene of canine distemper virus isolates over a period of almost four decades period revealed that the virus emerged in the United States in the late 1800s, with subsequent diversification and global spread, likely due to uncontrolled animal trade and human transfer of pets (Panzera et al. 2015). Spatial spread of pathogens is not necessarily associated with phylogenetic divergence. For example, in the case of Yersinia species, whole-genome sequencing has revealed a deep phylogenetic split between pathogenic strains and nonpathogenic lineages. However, all pathogenic Yersinia species do not share a recent common pathogenic ancestor, but rather they seem to have converged independently to acquire the same virulence determinants (Reuter 2015).

For pathogens that evolve rapidly, phylodynamics, an analytical method combining phylogenetics, epidemiology, population genetics, and immunology (Grenfell et al. 2004, Biek and Real 2010; Leventhal et al. 2012; Baele 2017, Volz et al. 2013), is a powerful approach that has been used to elucidate ecological (e.g., transmission bottlenecks; Volz 2017) and evolutionary (virulence heritability; Vrancken et al. 2014) dynamics. RNA viruses, are particularly well suited as markers of host ecology, because they are spread via direct contact and have higher mutation rates compared to the host genome (Volz 2013). Because of their relatively rapid rate of evolution, viruses can reveal patterns of host movement and provide insight into patterns of disease distribution and spread that may not be apparent in the host genetic data, and thus provide a complementary tool for studying population dynamics of their hosts in “shallow” time (Biek 2006). For example, genetic patterns associated with Feline Immunodeficiency Virus isolates from mountain lions (Puma concolor) in the Rocky Mountains of North America revealed pronounced spatial genetic structure providing information on the recent demographic history that was not evident from the host microsatellite data (Biek et al. 2006).

Species interactions: Characterizing disease emergence often entails a better understanding of when a parasite will spillover from one host species into another. Ecological filters critically affect disease emergence because host switching is more likely for sympatric host species or those that share overlapping niches. For example many emerging viruses in humans have wildlife reservoirs (e.g., SARS, corona, hanta and henipa viruses; Jones 2008). Consequently, characterization of viruses harbored by potential wildlife reservoirs is a potential tool to predict or prevent disease emergence. For example, a metagenomic survey of viruses in the urine of the Old World fruit bat, Eidolon helvum, revealed that bats roosting close to humans harbored a wide variety of viruses, some of which are genetically related to known human pathogens, highlighting the risk of zoonotic transmission (Baker 2013).

Whole genome sequence data has also been used to characterize the parasite transmission at the wildlife-domestic animal interface, including the transmission of bovine tuberculosis from badgers, Meles meles, to cattle (Biek et al. 2012) and the spatial spread of Brucella abortus among livestock, bison (Bison bison), and elk (Kamath 2016). As in the case of individual species (see above), bacterial transmission networks have also been used to characterize patterns of species interactions in multi-species communities. For example, genetic analysis of Escherichia coli across 10 species of wild and domestic ungulates in Kenya revealed that the Grant’s gazelle (Gazella granti) was the species that showed the most number of network connections in the community, while the zebra (Equus burchelli), which tends to move longer distances than many other ungulates in this system, was important in connecting regions of the network that would otherwise have been poorly connected (VanderWaal et al. 2014b).

Prospects: It is clear that epidemiological patterns relating to infection risk and parasite genetic structure can provide critical information relating to the structure and function of host populations and communities. Two of the major advantages provided by parasites compared to their hosts includes increased power due to improved sample sizes (Dharmarajan et al., 2016) and higher levels of genetic diversity due to increased effective population size and mutation rates (Volz 2013). These advantages are especially attractive when studying highly endangered species which are characterized by low population numbers and genetic diversity. However, the use of parasites as surrogates of host genetic structure is not without challenges. First, and foremost, is the issue related with sampling bias.

Parasites generally tend to show highly aggregated distributions (i.e., most hosts have few or no parasites and a few have high parasite intensities). This aggregated distribution leads to a “sample size double-whammy”: most hosts will provide few parasite samples while the number of hosts that provide a large parasite sample will be few. One obvious issue associated with parasite genetics is that uninfected hosts provide no information. Consequently an extreme bias associated with wildlife studies that depend on trapping live hosts is that parasites that kill their hosts or hosts with high infection burdens that die will be not be sampled (Artois 2009 in Delahay Book; see also Individual and Population Health below). Indeed, wildlife disease control measures (e.g., culling) can themselves directly reduce the trappability of animals (e.g., Tuyttens 1999). Another obvious issue associated with parasite genetics is the fact that parasite infection intensities are themselves driven by non-random factors. For example, it is well recognized that testosterone suppresses immune function and is also associated with other factors associated with parasite infection risk (e.g., territoriality; Ezenwa 2012).

Additionally, it remains unclear how immune function and dispersal will affect patterns across taxa. For example, while testosterone reduces immune function in both birds and mammals, dispersal is generally male biased dispersal in mammals and female biased in birds (Liberg 1985; Pussey 1987; but see Mabry 2013). Additionally, infection itself can impact dispersal dynamics. For example, changes in population density associated with Tasmanian devil facial tumor disease has led to altered gene flow among Tasmanian devil populations (Lachish et al. 2011, Bruniche-Olsen et al. 2013). Similarly, epidemic outbreaks of mange in California bobcats (##SPP) resulted in increased levels of inbreeding within and reduced geneflow amongst populations (Serieys et al. 2015).

A recent meta-analysis revealed that the correlation in the pairwise genetic differentiation of hosts and parasites is statistically significant, but surprisingly weak (Maze-Guilmo 2016). It has been recognized that multiple factors could affect the genetic structure of parasite populations, including life-history differences (e.g., generation time and overwinter survival; Schaik 2015) and fluctuating population size (Walton 2016). The characteristics of the parasites themselves also have to be matched to study objectives. For example, parasites with narrow host specificity are expected to show lower genetic diversity and stronger population structure as a result of limited dispersal, a pattern seen in multiple studies including those focusing on mites (Martinu 2018), lice (Martinu 2014), ticks vs. fleas (Wessels 2018) and avian haemosporidian parasites (Olsson-Pons 2015), and in the case of generalist (or multi-host parasites) the structure of parasite populations will likely be determined by the spatial scale of movement of its most mobile host (Louhi 2010; Criscione 2008; Feis 2015; Brouat 2011). Thus, specialist parasites are likely to better reflect the population structure of their hosts as compared to generalists (Criscione 2008). Alternatively, generalist parasites, which infect a wide diversity of hosts, will have to be studied when studying processes at the level of the host community rather than host species.

Finally, there is an urgent need to broaden the suite of genomic tools being used to study parasite genetics. Parasite genetic studies currently focus on one or a few related parasite lineages. However, more powerful genomic approaches, such as metagenomics, will not only facilitate pathogen discovery (Morse 2012; Lipkin 2013), but also facilitate the study of parasite communities in wildlife (Bodewes 2014) and disease vectors (Ma 2011). Additionally, while neutral molecular markers provide a powerful tool to characterize how gene flow and/or genetic drift shape population genetic structure, they do not provide insight on how selective forces, such as pathogens, drive adaptation (Manel and Holderegger 2013).

Evolutionary and co-evolutionary dynamics

Conservation biology has been described as a ‘crisis discipline’, and in many cases conservation biologists have to make the best decisions based on limited data (Soule 1985). The well-documented case of genetic restoration of the endangered Florida panther (Puma concolor coryi), highlights the challenges faced by conservation biologists on the ground. The Florida panther is a highly endangered subspecies restricted to one isolated population in south Florida, USA (USFWS 1995). In the early 1990’s studies revealed that the population was suffering from inbreeding depression, as characterized by low levels of genetic diversity in conjunction with numerous abnormalities (e.g., heart defects, gonadal abnormalities and low sperm count in males; Roelke 1993; Barone et al. 1994; Culver et al. 2000).

The decision to alleviate these issues with the genetic introgression of Texas puma (P. c. stanleyana) genes (USFWS 1995) was carefully studied and implemented (USFWS 1995; Hedrick 1995; Beier et al. 2003). Recent studies indicate that the decision has generally had positive impacts on Florida panther populations, in terms of increased abundance, genetic diversity and fitness (McBride et al. 2008; Johnson et al. 2010; Hostetler et al. 2010; Benson et al. 2011). However, the decision to reintroduce Texas pumas into Florida was a controversial one (Maehr & Caddick 1995). The controversy was based on an important issue: the relative importance of neutral genetic diversity (which would be improved with the introgression of Texas puma genes) vs. locally adapted gene complexes (which would be diluted with the introgression of Texas puma genes). The relative importance of neutral vs. adaptive genetic diversity has long been questioned in the field of conservation biology (e.g., Hughes 1991, Vrinjenhoek and Leberg 1991), and is one area where there has been a substantial contribution from the study of the ecological and evolutionary dynamics of parasites and disease.

Importance of neutral and adaptive genetic diversity: Parasites are a strong selective force because of their rapid evolutionary rate in conjunction with the negative fitness consequences associated with infection (Laddle 1995). It has been hypothesized that many costly behaviors (e.g., sexual reproduction) are maintained because they give hosts an advantage in adapting to rapidly evolving parasites (Laddle 1995; Morran 2011). Highly inbred populations or those with low levels of neutral genetic diversity are expected be more susceptible to disease due to reduced adaptive potential. Empirical evidence seems to support this perspective, and inbred individuals generally tend to have higher susceptibility to parasites compared to outbred ones (Hedrick et al., 2001). Marker-based heterozygosity at neutral loci has been shown to be negatively associated with susceptibility to parasites in insects (e.g. Whitehorn et al., 2011), birds (e.g. Ortego 2007a; Luikart 2008; Townsend 2018), mammals (e.g. Rijks et al., 2008) and fish (e.g. Hedrick 2001; Smallbone 2016; Eszterbauer et al., 2015). Inbreeding has also been associated with increased disease severity. For example, American crows (Corvus brachyrhynchos) that died with disease symptoms associated with West Nile Virus infections had higher inbreeding indices than birds with other fates (Townsend 2009). Similarly, European treefrogs (Hyla arborea) from inbred populations died more quickly when exposed to B. dendrobatidis compared to those from outbred populations (Luquet 2011).

However, the association between genetic diversity and parasitism is not universal, with some studies finding no association (Cote et al. 2005; Ortego 2007b), or variable effects based on the strength of parasite-mediated selective pressures (Ruiz-Lopez 2012). Additionally, since inbreeding is negatively correlated with genetic diversity (e.g., marker-based heterozygosity), it is generally assumed that these metrics will affect parasite infection in opposite directions. Interestingly, Mitchell (2017) found that while increased heterozygosity and was associated with lower parasite loads in wild banded mongooses (Mungos mungo), there was no association with the pedigree-based inbreeding coefficient, likely due linkage between genetic markers and genes influencing parasite burdens. Importantly, as genetic diversity increases there is also a concomitant increase in the prevalence of rare genotypes in the population.

The rarity of a particular host genotype could affect infection risk if pathogens tend to be co-adapted to more common genotypes, as predicted by the Red Queen hypothesis (##REF). However, few studies have explicitly tested the relative effects of genotype rarity and heterozygosity on infection risk in wildlife. In an elegant study, Eastwood 2017 show that both heterozygosity and genotype rarity are important in predicting beak and feather disease virus (BFDV) infections in wild parrots (Platycercus elegans). Importantly, these authors show that heterozygosity was negatively associated with infection risk, but not infection load, while host genotype rarity was associated with lower viral load in infected individuals, but did not predict infection risk (Eastwood 2017).

It has long been recognized that genes associated with immune function generally evolve more rapidly than other areas of the genome (Nielsen 2005; Lazzaro 2012). The major histocompatibility complex (MHC), a highly polymorphic family of genes involved in vertebrate immunity, is one of the best studied group of genes in wildlife (Acevedo-Whitehouse and Cunningham 2006, Radwan 2009). Numerous studies have reported relationships between MHC diversity and parasitism risk or disease severity in mammals (e.g., European badgers; Sin 2014), marsupials (e.g., Tasmanian devils; Caldwell 2017), reptiles (reviewed by Elbers 2016), amphibians (e.g., black-spotted pond frog, Pelophylax nigromaculatus; Li 2017) and fish (e.g., three‐spined sticklebacks, Gasterosteus aculeatus; Wegner 2003). However, these patterns are not universal, with no effects of MHC diversity on infection risk in New Zealand passerine birds (Sutton 2016) and giant pandas (##SPP, Zhang 2015).

Several studies have revealed that selection tends to maintain high MHC diversity even in populations that have undergone recent and severe bottlenecks that have dramatically reduced neutral genetic diversity in numerous species including: Hume’s pheasant, Syrmaticus humiae (Chen 2015), zebra finches, Taeniopygia guttata (Newhouse 2015), The lowland leopard frog, Lithobates yavapaiensis (Savage 2016), the alpine ibex, Capra ibex (Brambilla 2017), black-spotted pond frog, Pelophylax nigromaculatus (Li 2017) and European rabbits, Oryctolagus cuniculus (Schwensow 2017). Strong balancing selection on MHC loci has also been shown in some populations, thus maintaining relatively uniform MHC diversities, despite significant population genetic differentiation at neutral markers (Niskanen et al. 2014).

MHC diversity could also have important implications for community levels processes (e.g., interspecific competition). For example, the decline of red squirrel (Sciurus vulgaris) in the UK is likely due to apparent competition with the invasive eastern grey squirrel (S. carolinensis). Squirrelpox disease, caused by a viral infection carried asymptomatically by grey squirrels but to which red squirrels are highly susceptible. Typing of MHC loci in UK and continental European red squirrels indicate that red squirrel population in the UK have much lower MHC diversity compared with populations in continental Europe, a feature which may have contributed to their rapid decline (Ballingall 2016)

In the case of certain diseases, individual MHC alleles may be more important than overall MHC diversity (Niskanen et al. 2014). For example, particular conformations of the MHC class II PBR appear to confer resistance to the chytrid fungus and these alleles are shared among pathogen-resistant amphibian species globally (Fu 2017). Alternatively, it has been hypothesized that avian malaria can drive spatial (Loiseau et al. 2011) and temporal (Biedrzycka 2018) fluctuations in the MHC diversity of birds. Indeed, in song sparrows infection risk was lower for birds exposed to sympatric than to allopatric Plasmodium spp. lineages, suggesting that song sparrows may have a home-field advantage in defending against local parasite strains (Sarquis-Adamson 2016). However, it is possible that resistance to sympatric parasites stems from individuals’ prior immune experience rather than specific, locally protective, MHC alleles (Slade 2017).

While the MHC has been well studied factors affecting MHC diversity could be complex. For example, in a study of the common yellowthroat (Geothlypis trichas), Whittingham (2018) found that individuals in migratory populations had greater exposure or susceptibility to haemosporidians, and increased variation at MHC class I, but individuals in the resident population were less likely to be infected by haemosporidians and showed greater variation at MHC class II. These authors hypothesized that intracellular pathogens likely constitute a more important selective pressure for migrants, and extracellular pathogens constitute a more important selective pressure for residents (Whittingham 2018).

Additionally, it has been hypothesized that mate choice should tend to maximize MHC diversity in offspring to improve resistance to parasites (von Schantz et al. 1996) and survival (Abgali 2010). Meta‐analysis across a wide variety of taxa suggest that the direct effects of sexual selection may outweigh those associated with parasites in maintaining MHC variation (Winternitz et al. 2013), and there is a empirical evidence for both diversity- and dissimilarity-based mate choice in maintaining MHC diversity (Kamiya 2014).

Another critical issue is that much of our understanding of wildlife immunogenetics has been restricted to the MHC family of genes, and there is a need to expand the immune-genes studied in wildlife. Genomic data will enable epidemiological studies to identify variants at candidate immune or other genes hypothesized to be directly involved in resistance or tolerance (Brown and Knowles 2012). Many such candidate genes exist including: chemokine, interleukin and Toll-like receptors, as well as interferon and tumour necrosis factor genes (Acevedo-Whitehouse 2006). Some of these genes have been investigated for associations with disease or immune competence in wildlife. For instance, Turner et al. (2012) reported evidence that pathogen-influenced selection maintains genetic diversity in cytokines, genes critical for initiating and mediating the immune response, in field voles (Microtus agrestis). In a large-scale capture–mark –recapture study of wild bank voles (Myodes glareolus), Tschirren (2013) showed that individuals carrying a specific haplotypes in the Toll-like receptor gene TLR2 had a threefold lower risk of Borrelia spp. infection compared to animals carrying other haplotypes.

Additionally, a study by le Roex et al. (2013) revealed that three SNPs located in genes with predicted immune function were associated with bovine tuberculosis infection in the African buffalo (Syncerus caffer). Many studies in cervids also have shown that resistance to chronic wasting disease is associated with amino acid polymorphisms in the prion protein gene (Cheng 2017; Monello 2017; Brandt 2018). Finally, in one of the most comprehensive studies to date, Bateson (2016) showed that in the endangered Attwater’s prairie-chicken (Tympanuchus cupido attwateri), post-release survival of captive-bred birds was related to alleles of the innate (Toll-like receptors, TLRs) and adaptive (major histocompatibility complex, MHC) immune systems, but not to genome-wide heterozygosity across 20 990 SNPs.

Local adaptation: New population genomic approaches provide a powerful platform to test for patterns of local adaptation in natural populations by identifying areas of the genome that are under selection (e.g., by identifying loci that exhibit outlier allele frequencies) and the potential selection forces (e.g., environmental gradients in heterogeneous landscapes) (Manel & Holderegger 2013). Theory predicts that hot spots of coevolution critically impact evolutionary dynamics in selection mosaics (Gomulkiewicz 2000), and the best evidence for spatial variation in the strength of coevolution comes from several antagonistic systems, including host-parasite interactions (Laine and Tellier 2008).

Many groups of pathogenic organisms possess a ‘‘2-speed genome’’: a core genome consisting of essential genes that encode basal functions (e.g., cellular metabolism and reproduction) that is protected from high levels of recombination, mutation and insertion of novel genes, and an accessory genome consisting of genomic elements (plasmids, pathogenicity islands, accessory chromosomes, etc.) that evolve rapidly in response to antagonistic selection (Croll & McDonald 2012). Rapidly evolving parasites and the diseases they cause can apply strong and spatially variable selection pressures on natural populations, and thus these agents can be effectively used to study patterns of local adaptation in natural populations. For example, levels of geneflow likely is an important driver of local adaptation, and the relative levels of geneflow between species undergoing antagonistic selection is expected to determine which species will “win” the co-evolutionary arms race. Indeed, a metaanalysis has revealed that pathogens with greater dispersal ability than their hosts show a higher level of local adaptation to the host (Greischar & Koskella 2007).

Because host–parasite relationships are influenced by at least two interacting genomes, genetic correlations such as trade-offs between epidemiological and life-history traits are expected to be controlled by the interactions between host and parasite genotypes. Echaubard et al. (2014) showed that outcome of ranavirus infection depended on the particular combination of host and viral genotype. A significant genotype-by-genotype-by-environment interaction, demonstrating the potential for a selection mosaic, has also been shown in the case of Cryphonectria parasitica, the causal agent of chestnut blight, and its hyperparasitic virus, Cryphonectria hypovirus-1 (Bryner 2011).

Additionally, in a multi-year study of avian influenza viruses isolated from wild mallards found that individuals were rarely re-infected with the same or related hemagglutinin (HA) avian influenza subtypes, suggesting cross-protective immunity (Latorre-Margalef et al. 2013). The need to escape the immune system appears to have selected for genetic variation (subtypes) of the virus, leading to temporal patterns in the frequency of different HA avian influenza subtypes (Latorre-Margalef et al. 2014). In an elegant experiment using exposure-controlled infection trials with guppies (Poecilia reticulata) and an ectoparasite (Gyrodactylus turnbulli), Phillips (2018) provided direct evidence of a novel MHC variant advantage. The results of this study revealed that hosts carrying MHC variants (alleles or supertypes) that were new to a given parasite population experienced a 35–37% reduction in infection intensity, but the number of MHC variants carried by an individual was not a significant predictor of infection.

It is obvious that environmental factors will affect evolutionary dynamics. For example, transcriptomic data has revealed the underlying genetic mechanisms driving photoperiodic diapause in A. albopictus, a trait critical for adaptation to climatic variation and rapid range expansion in this species (Armbruster 2016). However, abiotic factors could also confound signatures associated with co-evolutionary dynamics between parasites and hosts. For example, the extrinsic incubation temperature and DENV-2 genotype both have direct effect on the infection rate in mosquitoes.

However, , consistent with previous studies. However, a recent study showed that extrinsic incubation temperature differentially impacts infection rate in different mosquito populations, indicating that the magnitude of DENV epidemics depends on an interaction between the virus and mosquito genotypes, as well as the local environment (Gloria-Soria 2017). It is also likely that abiotic rather than biotic factors impact apparent signals of local adaptation. For example, a study of a trematode ectoparasite (Gyrodactylus arcuatus) infecting threespined sticklebacks (Gasterosteus aculeatus) revealed highly variable levels of virulence and showed that parasites are generally locally adapted to their hosts. However, parasites also were adapted to the water in their own lake, and virulence is strongly related to lake pH (Mahmud 2017).

Individual and population health

Parasites and the diseases they cause negatively affect organisms at multiple levels of biological organization: individuals, populations and species (Daszak 2000; Smith 2008; Lachish 2011; Tompkins 2011) and alter the structure and function of ecological communities (see next section). At the individual level parasites directly impact survival: mortality of infected animals is 2.65 times higher than uninfected ones (see meta-analysis by Robar 2010). Parasites can also reduce fitness through sub-lethal effects: impaired reproduction, poor body condition, altered life-history parameters, and behavioral modifications (Minchella 1991; Hurtado 2008, Jones et al. 2008b, Lachish et al. 2009, Lee et al. 2010, Robinson et al. 2010; Stringer 2014). Parasites also can reduce fitness through indirect mechanisms (e.g., increased predation risk; Seppala 2008; Hatcher 2014).

Traditional models of parasite virulence generally assume that the amount of damage a parasite causes to its host are associated with the fact that parasites have to use host resources to reproduce, and thus transmit infections to new hosts (Frank 1996). Consequently, host-parasite interactions have traditionally been viewed as being antagonistic, with host fitness being maximized by resistance mechanisms through which the host reduces parasite burden (e.g. immune-mediated killing). However, it is increasingly being recognized that hosts can also minimize harm (fitness cost) inflicted at a given parasite burden through tolerance mechanisms (e.g. tissue repair; Raberg et al 2007; Schneider & Ayres 2008; Raberg et al. 2009; Rohr et al. 2010; Schneider and Schneider 2012). The importance of host tolerance in plants has long been recognized (Caldwell et al. 1958, Pagan 2018), but only recently, have animal ecologists started investigating the role tolerance plays in shaping host-parasite interactions (above). Pioneering studies have revealed that variation in tolerance in insects could be directed at altering the effects of parasites on survival (e.g., Drosophila melanogaster infected by the bacteria Salmon