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1 1259504 1 2 Title: Why infectious disease research needs community ecology 3 4 5 Authors: Pieter T. J. Johnson1*, Jacobus C. de Roode2, and Andy Fenton3 6 7 Affiliations: 8 1 Ecology and Evolutionary Biology, University of Colorado, Boulder, CO 80309 USA 9 2 Department of Biology, Emory University, Atlanta, GA 30322 USA 10 3 Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK 11 12 * Corresponding author. E-mail: [email protected] 2 13 Abstract 14 Infectious diseases often emerge from interactions among multiple species and across multiple 15 levels of biological organization. Threats ranging from Ebola virus to human malaria to bat 16 white-nose syndrome illustrate the need for a mechanistic understanding of the ecological 17 interactions underlying emerging infections. We describe how recent advances in community 18 ecology can be adopted to address contemporary challenges in disease research. These analytical 19 tools can identify the factors governing complex assemblages of multiple hosts, parasites, and 20 vectors, and reveal how processes link across scales from individual hosts to regions. They can 21 also determine the drivers of heterogeneities among individuals, species, and regions to aid 22 targeting of control strategies. We provide examples where these principles have enhanced 23 disease management and illustrate how they can be further extended. 3 24 Main text: 25 Despite notable successes (1, 2), infectious diseases remain a leading source of human morbidity 26 and mortality (3) and continue to threaten wildlife conservation and food production (4-6). A 27 common factor underlying emerging diseases is the involvement of multiple host, vector, or 28 parasite species in complex ecological communities. Nearly 70% of emerging human infectious 29 diseases have wildlife hosts or vectors (7, 8), while several human parasites have spilled over to 30 cause morbidity and mortality in wildlife, such as measles in mountain gorillas and tuberculosis 31 in Asian elephants (9). The use of multiple hosts by parasites complicates control efforts that 32 target particular hosts for management; for example, Schistosoma japonicum, the primary cause 33 of human schistosomiasis in Asia, can infect 120 different species of mammals (10). Similarly, 34 more than 20 species of triatomine bugs can transmit Trypanosoma cruzi, which causes Chagas 35 disease in South America, such that efforts to control the dominant vector species alone may be 36 inadequate to achieve elimination (11). Such threats continue to grow in importance as global 37 travel and human activities increase contact with novel sources of parasites, and aid their spread 38 across the globe (6). 39 Alongside the multi-host nature of many infections, interactions among multiple co- 40 infecting parasites can alter host pathology, parasite transmission, and virulence evolution (12- 41 14). Parasites that disrupt immune function, such as HIV, have facilitated the re-emergence of 42 drug-resistant forms of tuberculosis (15), while co-infection with parasitic worms (helminths) 43 such as hookworm, can exacerbate malaria (16). Interactions between several parasite species 44 have been similarly implicated in coral reef diseases, epidemics in plants, and marine mammal 45 die offs (17-19). Because many host-parasite interactions are intimately embedded within 46 communities of organisms, management efforts are sometimes thwarted by ‘ecological surprises’ 4 47 (20). Recent examples include the unexpected amplification of Middle East Respiratory 48 Syndrome coronavirus in internationally traded camels, and increased contact between badgers 49 and cattle following implementation of badger culling, ultimately leading to increased rather than 50 decreased transmission of bovine tuberculosis in the UK (21-23). Managing the challenges of 51 emerging infectious diseases thus requires a clear understanding of the full ecological context of 52 infection and transmission. 53 Our ability to understand and control infectious diseases has much to gain from the 54 discipline of community ecology, which has developed a range of analytical tools for addressing 55 complexity, species interactions and multi-level scaling (Fig. 1). These tools can be adopted to 56 improve our understanding and management of infectious diseases, both by quantifying 57 environmental and biological factors governing the structure of complex communities of 58 multiple hosts, vectors, and parasites, and also by identifying the effect and source(s) of 59 heterogeneity among individual hosts, host species, and geographic locations. These tools further 60 offer insight into interactions and feedbacks across multiple scales of organization, from within 61 hosts to across regions. We examine how the application of tools and concepts from community 62 ecology can help public health efforts to manage infectious disease threats. 63 64 Community ecology as a framework to understand infectious diseases 65 Community ecology offers a mechanistic bridge between processes unfolding at the fine scale of 66 individuals and populations and the ecological and evolutionary drivers of species distributions 67 at coarser scales. While some principles from community ecology have been applied to various 68 host-parasite systems (e.g., 24, 25-27), the ‘community ecology of disease’ remains in its relative 69 infancy, with most studies focusing on interactions between a single host and parasite species, 5 70 and often at a single scale. Data availability and quality are increasing rapidly, partly through 71 advances in sequencing technology, underscoring both the need for and the opportunity to 72 implement new methods to study infection dynamics in complex natural systems. 73 Community ecology theory tells us that, in parallel to the processes underlying 74 population genetics theory (i.e., gene flow, selection, drift, and mutation), the diversity, 75 abundance, and composition of species within a community can be understood in terms of 76 dispersal, ecological selection, ecological drift, and speciation (28). Following dispersal from the 77 regional species pool, a species’ success within a habitat is filtered by both niche- and stochastic 78 processes (29, 30). Within this framework, what needs to be understood is the degree to which 79 community structure is built predictably from niche-based effects associated with interactions 80 among species and the environment, or whether it arises through stochastic processes, such as 81 historical legacy, demographic stochasticity, and environmental fluctuations (Fig. 2). 82 Within their niche, parasites are affected by host condition, immune responses, the 83 abiotic environment and by interactions with co-infecting symbionts or associated free-living 84 organisms. If the assembly of parasite communities is predominantly deterministic, then the 85 richness and composition of parasite species will be predictable and based on characteristics of 86 the host and the environment. However, stochastic events and dispersal will also influence 87 parasite colonization-extinction dynamics. In some systems, for instance, the outcome of parasite 88 interactions depends strongly on the order of arrival within the host (14, 31). For example, long- 89 term sampling of wild field vole (Microtus agrestis) populations revealed that infection with the 90 protozoan Babesia microti reduced the probability that a host subsequently became infected with 91 the bacteria Bartonella spp. bacteria; however, if Bartonella established first, then B. microti was 92 75% less likely to invade (14). Similarly, high propagule dispersal by parasites can overcome 6 93 niche effects related to host susceptibility (32). For instance, although humans are dead-end hosts 94 with no onward transmission for many zoonotic infections, high exposure to such parasites can 95 have serious consequences for public health, such as West Nile encephalitis and late-stage Lyme 96 disease. Quantifying the relative contributions of niche-based and dispersal-based processes in 97 determining parasite community structure and individual infection risk offers an ecological 98 foundation for guiding resource investment into either defensive strategies, which focus on 99 altering niches to inhibit parasite establishment, or offensive strategies, which focus on limiting 100 dispersal (Fig. 3). 101 102 Approaches for understanding multi-level infection processes 103 Parasite metacommunities and assembly theory 104 Metacommunity theory provides a valuable toolkit for understanding the relative importance of 105 niche-based effects and dispersal-based effects in regulating the structure of parasite 106 communities (24, 33). By recognizing that landscapes support a series of ecological communities 107 connected through dispersal, metacommunity theory links interactions across local and regional 108 scales (32). For parasites, this framework can be applied to communities of parasites dispersing 109 among host individuals or across disjunct landscapes. Although rarely applied to parasite 110 communities, metacommunity-based approaches offer the potential to explore the interactive 111 roles of evolutionary history, dispersal limitation, host community composition and the abiotic 112 environment in driving parasite distributions (34) (Fig. 2). In a long-term study of 65 parasite 113 species from 15 species of desert rodents, for instance, Dallas and Presley (35) found that 114 parasite community structure was driven by niche effects associated with the ‘patch quality’ of 115 host species, including host traits such as body size, longevity, and abundance, rather than by 7 116 characteristics related to dispersal opportunities, such as host diet breadth or home range size, or 117 evolutionary history. In a study of plant parasites, Parker et al. (36) recently showed that 118 spillover risk in field experiments could be predicted by knowing the abundance of the host and 119 its phylogenetic relationships with other hosts in the community. In contrast to free-living 120 communities, parasite metacommunities do incur some unique analytical challenges, including 121 the potential for infections to sicken or kill individual hosts and thereby alter the availability of 122 habitat ‘patches’ for dispersal (26). Likewise, the co-assembly of host and parasite communities 123 needs to be examined concurrently (37), and an extra nested scale (i.e., for the within-host 124 dynamics) often needs to be included in analyses (Fig. 1). 125 Tools from network theory can be additionally valuable for understanding how 126 interactions between entire host and parasite communities vary over space and time (38). For 127 instance, Griffiths et al. (39) used network approaches to show that co-infecting parasites of 128 humans were organised into dense clusters around distinct locations in the body (e.g., organs), 129 and tended to interact with each other via shared resources within the host, rather than via the 130 immune system. Similar approaches have been applied across other scales of organization, for 131 example to define contact pathways for transmission among individual hosts (40, 41) and 132 identifying the role parasites play in structuring ecological food webs (42). While the focus of 133 network approaches thus far has often been on the patterns of links among species, emerging 134 tools allow for more explicit examination of interaction strengths which will help to forecast 135 dynamic changes in the system (43). 136 137 Infection heterogeneity and traits-based approaches 8 138 Community ecology emphasizes the importance of understanding individual and species-level 139 functional traits, thereby offering greater mechanistic and predictive power relative to simple 140 taxonomic classifications (44, 45). Although predicting the specific identities of species within 141 an assemblage is made difficult by stochastic factors, such as historical legacy, the composition 142 and frequency of functional traits may be more deterministic (46). Thus, while hosts and 143 parasites are typically defined in taxonomic terms, it may be more useful to classify them in 144 terms of functional traits that influence performance, such as transmission mode, site of 145 infection, or resource use for parasites, and body size, dispersal ability, or immune competence 146 for hosts. For instance, Han et al. (47) identified ‘trait profiles’ of known reservoir species and 147 used these to forecast candidate rodents likely to act as reservoirs for future zoonotic infections. 148 Their analysis emphasized the importance of ‘fast-paced’ species that reproduce early and often, 149 whereas taxonomic labels did a relatively poor job of classifying reservoir host status. 150 Trait-based analyses align with the long-standing recognition in disease ecology of the 151 disproportionate influence of superspreader individuals, amplification or reservoir host species, 152 or ‘hot-spot’ locations in driving transmission (22, 48, 49). Superspreading events have been 153 recorded for both wildlife and human diseases, including typhoid fever, HIV1, SARS and 154 tuberculosis (22, 50, 51), and can sometimes be linked to measurable variation in traits, such as 155 host immunity, behavior, age, diet and sex (52-54). For example, Perkins et al. (53) found that 156 large-bodied, sexually active male mice contribute 93% of potential transmission events for tick- 157 borne encephalitis virus, despite representing only ~20% of the host population. Methods to 158 partition the contributions of particular hosts, species, or locations to parasite transmission are 159 beginning to be developed (48, 55). For example, Rudge et al. (10) quantified host species 160 contributions to the number of cases generated (R0) of Schistosoma japonicum in China, for 9 161 which more than 120 host species have been identified. They showed that while bovids maintain 162 infection in marshlands, rodents are the main source of transmission in hilly areas, suggesting 163 that different control strategies are needed in the two habitats. The key challenge for 164 management is to identify how much of this heterogeneity is linked to measurable traits, and is 165 therefore predictable (niche-based), or whether it arises stochastically through unpredictable 166 temporal or spatial heterogeneity in exposure (56). 167 168 Moving across scales 169 A core principle of community ecology is the importance of scale in affecting the strength and 170 form of species interactions not only with each other but also with the environment (57)(Fig. 1). 171 Research in disease ecology often falls into one of three distinct levels: (1) within-host, which is 172 concerned with interactions with the host immune system and other parasites (13, 58); (2) 173 between-host, which is focused on parasite spread through host populations (59, 60) or, less 174 often, through host communities; or (3) on regional or biogeographical scales, which uses 175 comparative methods from macroecology to explore the drivers of parasite distributions and 176 diversity (61). 177 While studies focused on one scale often ignore, or treat as phenomenological black 178 boxes, the dynamics occurring at higher and lower scales, it appears that dynamic interactions 179 occur in both directions (41, 57). For instance, interactions among co-infecting parasites within 180 hosts can cause individual variation in susceptibility, infectiousness, behavior and survival (14, 181 62, 63), potentially with counter-intuitive consequences for transmission at the population level 182 (64). For example, African buffalo co-infected with gastrointestinal nematodes and bovine 183 tuberculosis (bTB) exhibit increased mortality, such that treating animals to reduce their worm 10 184 burdens improves individual survival but, by enabling infected hosts to live longer, is predicted 185 to increase population-level spread of bTB (63). Reciprocally, variation in host community 186 composition within a region can affect infection risk and spread at the individual and population 187 levels (10, 55). For vector-borne infections such as Lyme disease, wildlife species vary 188 considerably in their tendency to amplify the bacterium responsible and transmit it to suitable 189 tick vectors, such that regional variation in host species diversity is hypothesized to be a major 190 determinant of local infection risk for humans (65)(Box 1). However, such cross-scale processes 191 are hard to infer from observational data alone, and experimental perturbations are often needed 192 to definitively assess how processes at one scale affect those at another. In parallel with the rich 193 legacy of system manipulations from community ecology (66), disease ecologists have 194 increasingly used experimental approaches of natural systems, for example through anti-parasite 195 drug treatments (67), hormone manipulation (68), nutrient supplementation (69), and diversity 196 manipulations (70, 71). While often focused on single-host-single-parasite systems thus far, 197 implementing such experiments in more complex natural communities and at larger scales is 198 increasingly important for testing hypotheses about parasite transmission, impact and control. 199 200 How community ecology can help manage infectious diseases 201 We suggest that disease control strategies would benefit by incorporating community ecology 202 theory and approaches to explicitly account for the joint influences of dispersal and 203 environmental filters. Specifically, the “offensive” versus “defensive” concept developed for 204 invasive species can be applied to disease management (see also 72). Offensive strategies 205 allocate resources to limit the dispersal of an invader from established sites, whereas defensive 206 efforts reduce the vulnerability of uninvaded habitats to colonization (73). While this concept 11 207 parallels existing epidemiological emphasis on prevention versus control, its successful 208 application requires deeper insights into whether a parasite community is dispersal-limited, 209 niche-based, or is random in its assembly (Fig. 2). This approach can be used to strengthen 210 current methods of infectious disease management across the gamut of multi-host parasites, 211 multi-symbiont communities, and infection heterogeneities across scales (Fig. 3). 212 213 Managing multi-host parasites 214 A current pressing question is how ongoing changes in biodiversity will affect the spread and 215 severity of infectious diseases (74). When diverse communities also support species that interfere 216 with transmission, such as the presence of low susceptibility hosts, predators, or symbionts, 217 community structure can be manipulated defensively to manage infections by limiting niche 218 suitability (37). For example, zooprophylaxis (in which livestock are used as bait to divert blood- 219 feeding arthropod vectors away from people) has been proposed as a control strategy for vector- 220 borne diseases for over a century, but has had limited success in some settings because increased 221 livestock density can also increase vector abundance. However, recent models on malaria and 222 zoonotic cutaneous leishmaniasis indicate that carefully chosen livestock densities coupled with 223 insecticide treatment can effectively reduce parasite transmission to humans (75, 76). Similar 224 approaches, such as intercropping and crop rotation, have been used successfully to reduce plant 225 pests and parasites in agricultural systems (77). While evidence for such dilution effects 226 continues to grow (78), the degree to which biodiversity will regulate infection by a particular 227 parasite depends on the degree to which host assembly is deterministic, whether the parasite is 228 niche- or dispersal-limited, and how increases in richness affect host and vector abundance. 12 229 Managing host communities is also crucial to mitigating the risk of spillover events from 230 animal reservoirs to humans. To minimize the risks of spillover, there are several potential 231 offensive and defensive approaches, the choice of which will depend on the specific biology of 232 the hosts and vectors involved. The first option is to reduce infection in reservoir hosts. For 233 instance, vaccine baits have successfully eliminated rabies from several European countries 234 through their protective effects on non-human hosts (79). The second approach is to limit 235 contacts between wildlife and humans, for example by reducing bushmeat consumption and its 236 potential to introduce novel infections (80). In West Africa, for example, increasing the use of 237 alternative protein sources such as marine fish could relieve pressure on the bushmeat trade (81). 238 Such approaches require tight coordination between many parties, including medical scientists, 239 anthropologists and governments. Similarly, the use of transmission barriers can help to limit 240 contact between wildlife reservoirs and domestic animals (82, 83). The third approach is to 241 reduce the probability of infection when contact is unavoidable or unpredictable. Ongoing yet 242 unpredictable spillovers of dengue viruses from non-human primates, for instance, complicate 243 the control of human disease in Southeast Asia and Africa. One approach to control such 244 infections is through the implementation of cross-reactive vaccines, which are currently under 245 development (84). When vaccines are not available yet, as is the case for Ebola virus, reducing 246 human-human transmission through contact tracing and subsequent quarantine and treatment can 247 help to limit epidemic spread (85). 248 249 Managing symbiont communities 250 Interactions among co-infecting parasites or symbionts can also be used as niche-based 251 management tools. For example, treating patients suffering from lymphatic filariasis with the 13 252 antibiotic doxycycline eliminates essential symbiotic bacteria required by filarial worms, 253 ultimately leading to worm sterility and death (86). Restoration or augmentation of the microbial 254 community within the host can also provide protection against parasite invasion. For example, 255 transferring human-microbial communities by fecal transplants often leads to clinical resolution 256 of intestinal pathology associated with Clostridium difficile infection (87). Finally, interactions 257 among co-infecting parasites, parasite strains, or other symbionts can be manipulated to reduce 258 the spread of disease-causing organisms. Long-lived parasites, such as helminths, may 259 exacerbate disease caused by co-infecting parasites, leading to calls to incorporate de-worming 260 to improve management of HIV, malaria and TB (16, 88). In other cases, antagonistic 261 interactions between parasites or other symbionts may be used to benefit the host. For instance, 262 trials are under development to reduce the vector competence of mosquitoes by infecting them 263 with the bacterium Wolbachia, which inhibits Dengue virus and filarial worm survival and 264 transmission through a combination of immune activation, competition for cellular components, 265 and shortened mosquito lifespan (89, 90). These examples emphasize the importance of 266 understanding and predicting the outcome of multiple infections, for which community ecology 267 approaches focused on parasite traits and resource use have already offered added insights (91). 268 269 Heterogeneity and scale 270 The disproportionate roles of particular locations, particular host species, and particular 271 individual hosts in driving epidemics or epizootics raises the tantalizing promise of highly 272 efficient targeted control and treatment (22, 48, 50). In the Serengeti, for example, where rabies 273 can infect up to 12 carnivore species, domestic dogs are responsible for more than 70 percent of 274 transmission events to humans (92). Annual vaccination of 60 percent of dogs is projected to 14 275 control the virus, a target that is logistically and economically feasible (93). During the recent 276 Ebola epidemic in West Africa, close contact between deceased patients and family or friends 277 during traditional burials functioned as superspreading events (94), and implementation of 278 ‘sanitary burials’ that reduced such contacts helped curb the epidemic. Thus, targeting 279 superspreading hosts or events is feasible when transmission heterogeneities are deterministic 280 and can be linked to measurable traits or characteristics. 281 Ultimately, the efficacy of offensive and defensive approaches will depend on whether 282 the scale of application is local or regional, the transmission and dispersal characteristics of the 283 parasite involved, and the point in the epidemic when the intervention is initiated and how many 284 individuals have already been infected (72). Defensive, niche-based management strategies, 285 ranging from vaccination and prophylaxis to ecological competition by probiotic symbionts, are 286 more likely to be effective when parasite dispersal is high, for parasites with high or 287 unpredictable propagule pressure, and for epidemics already underway (Fig. 3). In contrast, 288 offensive strategies that focus on reducing dispersal are more likely to succeed at community and 289 regional scales than at individual and population scales, because parasite dispersal between 290 individuals within a host population is often harder to control than dispersal between sites. For 291 instance, established populations of the Asian tiger mosquito, Aedes albopictus, recently linked 292 to a large outbreak of the viral disease chikungunya on the Indian Ocean Island La Réunion (95), 293 are almost impossible to eliminate; however, because most introductions of this vector have 294 occurred through the shipment of used tires, focused efforts to limit this trade offer the best 295 potential for containing future spread of the vector (95). 296 297 Outlook 15 298 While the disciplines of epidemiology and community ecology have developed largely 299 independently from one another, the multi-species nature of many contemporary disease threats 300 demands a community-scale approach to complement more traditional biomedical treatments. 301 Community ecology offers a theoretical framework and the analytical tools to move beyond the 302 historical emphasis on particular host-parasite interactions and consider the full suite of species 303 that influence infection dynamics. We have emphasized approaches from community ecology 304 that can advance our ability to manage infections by: (1) identifying the factors that govern the 305 structure and dynamics of communities composed of multiple hosts, vectors, and symbionts, (2) 306 identifying the drivers of heterogeneity, and (3) understanding how processes and patterns link 307 across multiple scales of biological organization. For many emerging infections, complete 308 eradication is unlikely to be successful, but a broader understanding of the ecological 309 communities in which host-parasite interactions are embedded will facilitate more effective 310 management. 311 Transforming this broader understanding into practical disease management requires tight 312 integration of surveillance, community ecology analysis, and public health implementation (96). 313 Ongoing technological advances are rapidly overcoming previous barriers in data quality and 314 quantity, highlighting emerging opportunities to incorporate approaches from community 315 ecology into existing disease research and evaluate the factors driving the structure and dynamics 316 of natural disease systems. Combining analyses of these high-resolution data with modeling 317 approaches and large-scale manipulations of host-parasite interactions – similar to the 318 foundational experiments from community ecology (66) – offer among the best opportunities for 319 developing a deeper understanding of the processes underlying disease emergence and control. 320 To date there have been some practical successes that follow this broad approach. For example, 16 321 following the observation of five dead howler monkeys - a key host for yellow fever virus - a 322 collaborative effort between the USAID PREDICT program and the Bolivian government led to 323 rapid implementation of human vaccination and mosquito control in the affected area (97). 324 Similarly, increased use of buffer zones between fruit trees and livestock housing has been 325 effectively used in Malaysia to reduce Nipah virus transmission into pigs and the risks of human 326 outbreaks (82), while electrified fences in Kruger National Park have helped limit contact 327 between bovine TB-infected wildlife and cattle in surrounding areas (83). 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PLoS Biol. 7, 462-471 (2009). 22 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 94. 608 Acknowledgments 609 For discussions and feedback helpful in shaping the manuscript, we thank S. Altizer, D. Calhoun, 610 G. Devevey, I. Doron, S. Haas, K. Hoang, B. Hoye, M. Joseph, J. Koprivnikar, T. McDevitt- 611 Galles, J. Mihaljevic, A. Pedersen, O. Petchey, A. Pierce, D. Preston, Y. Springer, W. Stutz, L. 612 Tao and S. White. PTJJ was supported by funds from NSF (DEB-1149308) and NIH 613 (R01GM109499), JCdR was supported by funds from NSF (DEB-1257160) and NIH 614 (R01GM109501), and AF was supported by funding from the Natural Environment Research 615 Council (NERC) UK (NE/G006830/1 and NE/I024038/1). 616 95. 96. 97. 98. 99. 100. 101. 102. 103. A. Pandey, K. E. Atkins, J. Medlock, N. Wenzel, J. P. Townsend, J. E. Childs, T. G. Nyenswah, M. L. Ndeffo-Mbah, A. P. Galvani, Strategies for containing Ebola in West Africa. 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Laurenson, H. W. Reid, P. J. Hudson, Disease persistence and apparent competition in a three-host community: an empirical and analytical study of large-scale, wild populations. J. Anim. Ecol. 70, 1053-1061 (2001). R. Norman, R. G. Bowers, M. Begon, P. J. Hudson, Persistence of tick-horne virus in the presence of multiple host species: Tick reservoirs and parasite mediated competition. J. Theor. Biol. 200, 111-118 (1999). C. L. Wood, K. D. Lafferty, Biodiversity and disease: a synthesis of ecological perspectives on Lyme disease transmission. Trends Ecol. Evol. 28, 239-247 (2013). 23 617 Boxes and Figures 618 Box 1. The role of simple theory in disease ecology and its extension to complex communities 619 620 Fig. 0. The community ecology of disease. Coinfection by (A) nematodes increases host 621 mortality due to (B) bovine TB among (C) African buffalo (63); Tsimane villagers (D) in Bolivia 622 reveal negative correlations between (E) Giardia lamblia and (F) Ascaris lumbricoides, where 623 de-worming increased Giardia (98). For tick-borne encephalitis (G), 93% of transmission events 624 involve large-bodied, male yellow-necked mice (H), which comprise <20% of the population 625 (53). For humans, disproportionate contact among individuals (I) can lead to ‘superspreading 626 events’ for SARS (J) (50). Among-species heterogeneities can alter community-wide 627 transmission; Crayfish plague (K) introduced to Europe with red swamp crayfish (L) led to 628 native crayfish declines; white-footed mice (M) are highly competent hosts for Borrelia 629 burgdorferi (N) and influence production of infected ticks that transmit Lyme borreliosis (65). 630 Image credits: CDC, R. Grencis, Y. Krishnappa, A. Pisor, CDC, F. Dubs, Stiasny et al. (2007), 631 V. Dostál, CDC, CDC, T. Vrålstad, F. Pupin, J. Brunner and NIH. 632 633 Fig. 1. Ecological hierarchies applied to host-parasite interactions and analogous processes in 634 community ecology. Included scales range from within-host (‘parasite infracommunity’, often 635 dominated by parasite-parasite and parasite-immune system interactions) to between-host 636 (‘parasite component community’, population biology) to among species (‘parasite 637 supracommunity’, community ecology) to across regions (macroecology and disease 638 biogeography). The different colored squares represent different parasite species, while the text 639 on the right and left sides of the figure highlight the relevant processes from community ecology 24 640 and disease ecology, respectively. The potential importance for interactions and feedback across 641 these scales represents an essential research frontier in the field of disease community ecology. 642 643 Fig. 2. Parasite community assembly depends on a combination of ecological selection (A), 644 ecological drift (B), and dispersal (C). Following input via dispersal (indicated as arrows from 645 the parasite regional pool), parasite establishment depends on ecological selection: different 646 species (mice versus prairie dogs) select for different parasites based on genetics, behavior, 647 immune status and other host properties (including vaccination status/drug presence). 648 Deterministic, within-host parasite interactions (indicated by + and – signs) are an additional 649 niche-based influence on parasite communities; positive parasite interactions (facilitation) are 650 indicated by thicker arrows, while negative interactions are indicated by dashed arrows. (B) 651 Parasite community assembly is also influenced by ecological drift (stochasticity), particularly 652 when colonizing populations are small or the outcome of parasite interactions depends on their 653 order of arrival (‘priority effects’). As a result, parasite communities can appear random with 654 respect to host species or type, even if strongly affected by species interactions. (C) High rates of 655 dispersal can swamp niche effects and overwhelm stochasticity, resulting in more similar 656 parasite communities across hosts, regardless of host species. For simplicity, no feedback loops 657 are shown from the individual hosts back to the parasite pool, although understanding such 658 feedbacks is an important research frontier (Fig. 3). 659 660 Fig. 3. (A) Using community ecology-based management strategies for infectious disease. 661 Levels of ecological organization are shown in the middle, and colored arrows indicate the 662 ecological processes that connect these levels. Parasite dispersal connects scales going up 25 663 through the hierarchy, while parasite establishment connects scales moving down the hierarchy. 664 Grey shaded arrows indicate the relative importance of offensive strategies (preventing parasite 665 dispersal) and defensive strategies (preventing parasite establishment), with darker shades 666 reflecting greater importance. (B) Management strategies focused on reducing spillover from 667 wildlife to humans (zoonosis) and from humans to wildlife (anthronosis or reverse zoonosis). 668 Probability of spillover and subsequent spread of infection can be reduced through four major 669 strategies. First, control may focus on reducing disease prevalence in reservoir hosts; for 670 instance, vaccine baits have been successfully used to eliminate rabies from several European 671 countries (79). Second, contact rates can be reduced between humans and wild animals (8); for 672 example, limiting the proximity between humans and wildlife can reduce spillover of human 673 illnesses, such as measles, tuberculosis, and MRSA, to wildlife. Third, zoonotic risk can be 674 reduced by lowering the probability of infection when contact is unavoidable or unpredictable. 675 For instance, some human Dengue vaccine candidates provide cross-protection against sylvatic 676 Dengue viruses, which naturally circulate in non-human primates (84). Finally, when spillover 677 does occur, regional control strategies – including isolating infected populations, dispatching 678 medical personnel and aid, and enhanced border control – can be used to prevent disease 679 transmission across borders. 680 26 681 Box 1: The role of simple theory in disease ecology and its extension to complex 682 communities 683 The pioneering work of Anderson and May (60, 99) formalized our understanding of parasite 684 dynamics by highlighting the importance of the basic reproductive number (R0) as a measure of 685 whether a parasite will spread through a population (R0 > 1) or die out (R0 < 1). The fundamental 686 principles of these basic models – initially developed for single host-single parasite systems – 687 can provide insight into infection dynamics in more complex ecological systems. For example. 688 parasites often face a diverse community of potential host species, which differ in abundance, 689 susceptibility and infectiousness. Simple extensions of basic disease ecology theory can 690 determine the conditions under which one host species amplifies or dilutes infection risk for 691 other species in the community. For directly transmitted parasites, or even those transmitted via 692 infective stages in the environment, theory shows that the parasite's overall basic reproductive 693 number among the available host community (R0,TOT) can simply be proportional to the sum of 694 the R0 for each host species alone, provided there is equal mixing within and between host 695 species (although other relationships between the individual-level and community-level R0 696 values may occur if mixing is not equal) (10, 100). Hence, there is a clear connection between 697 this more complex scenario and the classical single-host theory. 698 This theory can be extended further for vector-borne parasites, which become 699 complicated if hosts differ in their relative competencies for the parasite and the vector. For 700 example, tick-borne parasites may involve a mammalian host species that is parasite-competent 701 but cannot support tick reproduction, and another mammalian species that is non-competent for 702 the parasite but essential for tick reproduction (Box 1, Fig. 1). Here there are three possible 703 outcomes: (1) tick-and-parasite exclusion, (2) tick persistence but parasite exclusion and (3) tick- 27 704 and-parasite persistence, depending on different combinations of the R0 values for the parasite 705 (R0,parasite) and the tick (R0,tick). Ultimately, this results in outcomes that are nonlinearly related to 706 the density of the non-competent host; initial increases in non-competent host abundance (N) can 707 cause vector amplification leading to increased parasite R0, whereas high N dilutes transmission 708 through ‘wasted’ bites on the non-competent host (101-103). 709 710 Figure legend for Box 711 Model of a tick-borne parasite system with two host species, showing potential for both 712 amplification and dilution within the same system. (A) Schematic diagram of the model, where 713 one host species (C) is parasite-competent but cannot support tick reproduction, and the other (N) 714 is non-competent but essential for tick reproduction. This system can be described by the 715 equations (modified from (101-103)): 716 d𝐶p = (1 − 𝐶p )𝛽1 𝑇𝐼 − 𝛿𝑅 𝐶P d𝑡 717 d𝑇 = (𝑎 𝑇 − 𝑠𝑇 𝑇)𝑇𝛽3 𝑁 − 𝛿𝑇 𝑇 d𝑡 718 d𝑇𝐼 = (𝑇 − 𝑇𝐼 )𝛽2 𝐶𝐶𝑝 − 𝛿𝑇 𝑇𝐼 − 𝑇𝐼 𝛽3 𝑁 d𝑡 719 where T is the total number of ticks, (TU in the figure is the number of uninfected ticks; TI is the 720 number of infected ticks), CP is parasite prevalence within C (CU in the figure is the number of 721 uninfected hosts; CI is the number of infected hosts), β1 is tick C transmission rate of the 722 parasite, β2 is C tick transmission rate of the parasite, β3 is tick N biting rate, the δi are 723 respective mortality rates, aT is tick reproduction rate and sT is the strength of tick density 724 dependence. (B) Phase plot of Competent host (C) – Non-competent host (N) densities, showing 725 the three regions of dynamical outcome, separated by the boundaries of R0,tick=1 and R0,parasite=1, 28 𝑎𝑇 𝛽3 𝑁 𝐶𝛽1 𝛽2 (𝑎𝑇 𝛽3 𝑁−𝛿𝑇 ) 726 where 𝑅0,𝑡𝑖𝑐𝑘 = 727 competent host density, showing that low host densities facilitate parasite transmission due to 728 vector amplification whereas high host densities reduce parasite transmission through wasted 729 tick bites. The vertical line marked N’ (given by the value of N at which 𝑅0,𝑡𝑖𝑐𝑘 = 1 + √1 + 𝑎 𝑇 ) 730 shows the non-competent host density at which the effect on the parasite switches from 731 amplification to dilution. 𝛿𝑇 and 𝑅0,𝑝𝑎𝑟𝑎𝑠𝑖𝑡𝑒 = 𝑠 . (C) R0,parasite as a function of non- 𝑇 𝛽3 𝑁𝛿𝐶 (𝛿𝑇 +𝛽3 𝑁)