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Title: Why infectious disease research needs community ecology
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Authors: Pieter T. J. Johnson1*, Jacobus C. de Roode2, and Andy Fenton3
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Affiliations:
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Ecology and Evolutionary Biology, University of Colorado, Boulder, CO 80309 USA
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Department of Biology, Emory University, Atlanta, GA 30322 USA
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Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
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* Corresponding author. E-mail: [email protected]
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Abstract
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Infectious diseases often emerge from interactions among multiple species and across multiple
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levels of biological organization. Threats ranging from Ebola virus to human malaria to bat
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white-nose syndrome illustrate the need for a mechanistic understanding of the ecological
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interactions underlying emerging infections. We describe how recent advances in community
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ecology can be adopted to address contemporary challenges in disease research. These analytical
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tools can identify the factors governing complex assemblages of multiple hosts, parasites, and
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vectors, and reveal how processes link across scales from individual hosts to regions. They can
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also determine the drivers of heterogeneities among individuals, species, and regions to aid
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targeting of control strategies. We provide examples where these principles have enhanced
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disease management and illustrate how they can be further extended.
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Main text:
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Despite notable successes (1, 2), infectious diseases remain a leading source of human morbidity
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and mortality (3) and continue to threaten wildlife conservation and food production (4-6). A
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common factor underlying emerging diseases is the involvement of multiple host, vector, or
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parasite species in complex ecological communities. Nearly 70% of emerging human infectious
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diseases have wildlife hosts or vectors (7, 8), while several human parasites have spilled over to
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cause morbidity and mortality in wildlife, such as measles in mountain gorillas and tuberculosis
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in Asian elephants (9). The use of multiple hosts by parasites complicates control efforts that
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target particular hosts for management; for example, Schistosoma japonicum, the primary cause
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of human schistosomiasis in Asia, can infect 120 different species of mammals (10). Similarly,
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more than 20 species of triatomine bugs can transmit Trypanosoma cruzi, which causes Chagas
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disease in South America, such that efforts to control the dominant vector species alone may be
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inadequate to achieve elimination (11). Such threats continue to grow in importance as global
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travel and human activities increase contact with novel sources of parasites, and aid their spread
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across the globe (6).
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Alongside the multi-host nature of many infections, interactions among multiple co-
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infecting parasites can alter host pathology, parasite transmission, and virulence evolution (12-
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14). Parasites that disrupt immune function, such as HIV, have facilitated the re-emergence of
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drug-resistant forms of tuberculosis (15), while co-infection with parasitic worms (helminths)
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such as hookworm, can exacerbate malaria (16). Interactions between several parasite species
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have been similarly implicated in coral reef diseases, epidemics in plants, and marine mammal
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die offs (17-19). Because many host-parasite interactions are intimately embedded within
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communities of organisms, management efforts are sometimes thwarted by ‘ecological surprises’
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(20). Recent examples include the unexpected amplification of Middle East Respiratory
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Syndrome coronavirus in internationally traded camels, and increased contact between badgers
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and cattle following implementation of badger culling, ultimately leading to increased rather than
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decreased transmission of bovine tuberculosis in the UK (21-23). Managing the challenges of
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emerging infectious diseases thus requires a clear understanding of the full ecological context of
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infection and transmission.
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Our ability to understand and control infectious diseases has much to gain from the
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discipline of community ecology, which has developed a range of analytical tools for addressing
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complexity, species interactions and multi-level scaling (Fig. 1). These tools can be adopted to
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improve our understanding and management of infectious diseases, both by quantifying
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environmental and biological factors governing the structure of complex communities of
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multiple hosts, vectors, and parasites, and also by identifying the effect and source(s) of
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heterogeneity among individual hosts, host species, and geographic locations. These tools further
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offer insight into interactions and feedbacks across multiple scales of organization, from within
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hosts to across regions. We examine how the application of tools and concepts from community
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ecology can help public health efforts to manage infectious disease threats.
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Community ecology as a framework to understand infectious diseases
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Community ecology offers a mechanistic bridge between processes unfolding at the fine scale of
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individuals and populations and the ecological and evolutionary drivers of species distributions
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at coarser scales. While some principles from community ecology have been applied to various
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host-parasite systems (e.g., 24, 25-27), the ‘community ecology of disease’ remains in its relative
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infancy, with most studies focusing on interactions between a single host and parasite species,
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and often at a single scale. Data availability and quality are increasing rapidly, partly through
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advances in sequencing technology, underscoring both the need for and the opportunity to
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implement new methods to study infection dynamics in complex natural systems.
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Community ecology theory tells us that, in parallel to the processes underlying
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population genetics theory (i.e., gene flow, selection, drift, and mutation), the diversity,
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abundance, and composition of species within a community can be understood in terms of
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dispersal, ecological selection, ecological drift, and speciation (28). Following dispersal from the
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regional species pool, a species’ success within a habitat is filtered by both niche- and stochastic
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processes (29, 30). Within this framework, what needs to be understood is the degree to which
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community structure is built predictably from niche-based effects associated with interactions
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among species and the environment, or whether it arises through stochastic processes, such as
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historical legacy, demographic stochasticity, and environmental fluctuations (Fig. 2).
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Within their niche, parasites are affected by host condition, immune responses, the
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abiotic environment and by interactions with co-infecting symbionts or associated free-living
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organisms. If the assembly of parasite communities is predominantly deterministic, then the
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richness and composition of parasite species will be predictable and based on characteristics of
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the host and the environment. However, stochastic events and dispersal will also influence
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parasite colonization-extinction dynamics. In some systems, for instance, the outcome of parasite
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interactions depends strongly on the order of arrival within the host (14, 31). For example, long-
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term sampling of wild field vole (Microtus agrestis) populations revealed that infection with the
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protozoan Babesia microti reduced the probability that a host subsequently became infected with
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the bacteria Bartonella spp. bacteria; however, if Bartonella established first, then B. microti was
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75% less likely to invade (14). Similarly, high propagule dispersal by parasites can overcome
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niche effects related to host susceptibility (32). For instance, although humans are dead-end hosts
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with no onward transmission for many zoonotic infections, high exposure to such parasites can
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have serious consequences for public health, such as West Nile encephalitis and late-stage Lyme
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disease. Quantifying the relative contributions of niche-based and dispersal-based processes in
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determining parasite community structure and individual infection risk offers an ecological
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foundation for guiding resource investment into either defensive strategies, which focus on
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altering niches to inhibit parasite establishment, or offensive strategies, which focus on limiting
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dispersal (Fig. 3).
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Approaches for understanding multi-level infection processes
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Parasite metacommunities and assembly theory
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Metacommunity theory provides a valuable toolkit for understanding the relative importance of
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niche-based effects and dispersal-based effects in regulating the structure of parasite
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communities (24, 33). By recognizing that landscapes support a series of ecological communities
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connected through dispersal, metacommunity theory links interactions across local and regional
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scales (32). For parasites, this framework can be applied to communities of parasites dispersing
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among host individuals or across disjunct landscapes. Although rarely applied to parasite
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communities, metacommunity-based approaches offer the potential to explore the interactive
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roles of evolutionary history, dispersal limitation, host community composition and the abiotic
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environment in driving parasite distributions (34) (Fig. 2). In a long-term study of 65 parasite
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species from 15 species of desert rodents, for instance, Dallas and Presley (35) found that
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parasite community structure was driven by niche effects associated with the ‘patch quality’ of
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host species, including host traits such as body size, longevity, and abundance, rather than by
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characteristics related to dispersal opportunities, such as host diet breadth or home range size, or
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evolutionary history. In a study of plant parasites, Parker et al. (36) recently showed that
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spillover risk in field experiments could be predicted by knowing the abundance of the host and
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its phylogenetic relationships with other hosts in the community. In contrast to free-living
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communities, parasite metacommunities do incur some unique analytical challenges, including
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the potential for infections to sicken or kill individual hosts and thereby alter the availability of
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habitat ‘patches’ for dispersal (26). Likewise, the co-assembly of host and parasite communities
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needs to be examined concurrently (37), and an extra nested scale (i.e., for the within-host
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dynamics) often needs to be included in analyses (Fig. 1).
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Tools from network theory can be additionally valuable for understanding how
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interactions between entire host and parasite communities vary over space and time (38). For
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instance, Griffiths et al. (39) used network approaches to show that co-infecting parasites of
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humans were organised into dense clusters around distinct locations in the body (e.g., organs),
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and tended to interact with each other via shared resources within the host, rather than via the
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immune system. Similar approaches have been applied across other scales of organization, for
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example to define contact pathways for transmission among individual hosts (40, 41) and
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identifying the role parasites play in structuring ecological food webs (42). While the focus of
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network approaches thus far has often been on the patterns of links among species, emerging
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tools allow for more explicit examination of interaction strengths which will help to forecast
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dynamic changes in the system (43).
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Infection heterogeneity and traits-based approaches
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Community ecology emphasizes the importance of understanding individual and species-level
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functional traits, thereby offering greater mechanistic and predictive power relative to simple
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taxonomic classifications (44, 45). Although predicting the specific identities of species within
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an assemblage is made difficult by stochastic factors, such as historical legacy, the composition
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and frequency of functional traits may be more deterministic (46). Thus, while hosts and
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parasites are typically defined in taxonomic terms, it may be more useful to classify them in
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terms of functional traits that influence performance, such as transmission mode, site of
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infection, or resource use for parasites, and body size, dispersal ability, or immune competence
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for hosts. For instance, Han et al. (47) identified ‘trait profiles’ of known reservoir species and
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used these to forecast candidate rodents likely to act as reservoirs for future zoonotic infections.
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Their analysis emphasized the importance of ‘fast-paced’ species that reproduce early and often,
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whereas taxonomic labels did a relatively poor job of classifying reservoir host status.
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Trait-based analyses align with the long-standing recognition in disease ecology of the
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disproportionate influence of superspreader individuals, amplification or reservoir host species,
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or ‘hot-spot’ locations in driving transmission (22, 48, 49). Superspreading events have been
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recorded for both wildlife and human diseases, including typhoid fever, HIV1, SARS and
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tuberculosis (22, 50, 51), and can sometimes be linked to measurable variation in traits, such as
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host immunity, behavior, age, diet and sex (52-54). For example, Perkins et al. (53) found that
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large-bodied, sexually active male mice contribute 93% of potential transmission events for tick-
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borne encephalitis virus, despite representing only ~20% of the host population. Methods to
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partition the contributions of particular hosts, species, or locations to parasite transmission are
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beginning to be developed (48, 55). For example, Rudge et al. (10) quantified host species
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contributions to the number of cases generated (R0) of Schistosoma japonicum in China, for
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which more than 120 host species have been identified. They showed that while bovids maintain
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infection in marshlands, rodents are the main source of transmission in hilly areas, suggesting
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that different control strategies are needed in the two habitats. The key challenge for
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management is to identify how much of this heterogeneity is linked to measurable traits, and is
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therefore predictable (niche-based), or whether it arises stochastically through unpredictable
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temporal or spatial heterogeneity in exposure (56).
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Moving across scales
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A core principle of community ecology is the importance of scale in affecting the strength and
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form of species interactions not only with each other but also with the environment (57)(Fig. 1).
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Research in disease ecology often falls into one of three distinct levels: (1) within-host, which is
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concerned with interactions with the host immune system and other parasites (13, 58); (2)
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between-host, which is focused on parasite spread through host populations (59, 60) or, less
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often, through host communities; or (3) on regional or biogeographical scales, which uses
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comparative methods from macroecology to explore the drivers of parasite distributions and
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diversity (61).
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While studies focused on one scale often ignore, or treat as phenomenological black
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boxes, the dynamics occurring at higher and lower scales, it appears that dynamic interactions
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occur in both directions (41, 57). For instance, interactions among co-infecting parasites within
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hosts can cause individual variation in susceptibility, infectiousness, behavior and survival (14,
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62, 63), potentially with counter-intuitive consequences for transmission at the population level
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(64). For example, African buffalo co-infected with gastrointestinal nematodes and bovine
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tuberculosis (bTB) exhibit increased mortality, such that treating animals to reduce their worm
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burdens improves individual survival but, by enabling infected hosts to live longer, is predicted
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to increase population-level spread of bTB (63). Reciprocally, variation in host community
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composition within a region can affect infection risk and spread at the individual and population
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levels (10, 55). For vector-borne infections such as Lyme disease, wildlife species vary
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considerably in their tendency to amplify the bacterium responsible and transmit it to suitable
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tick vectors, such that regional variation in host species diversity is hypothesized to be a major
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determinant of local infection risk for humans (65)(Box 1). However, such cross-scale processes
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are hard to infer from observational data alone, and experimental perturbations are often needed
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to definitively assess how processes at one scale affect those at another. In parallel with the rich
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legacy of system manipulations from community ecology (66), disease ecologists have
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increasingly used experimental approaches of natural systems, for example through anti-parasite
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drug treatments (67), hormone manipulation (68), nutrient supplementation (69), and diversity
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manipulations (70, 71). While often focused on single-host-single-parasite systems thus far,
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implementing such experiments in more complex natural communities and at larger scales is
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increasingly important for testing hypotheses about parasite transmission, impact and control.
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How community ecology can help manage infectious diseases
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We suggest that disease control strategies would benefit by incorporating community ecology
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theory and approaches to explicitly account for the joint influences of dispersal and
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environmental filters. Specifically, the “offensive” versus “defensive” concept developed for
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invasive species can be applied to disease management (see also 72). Offensive strategies
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allocate resources to limit the dispersal of an invader from established sites, whereas defensive
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efforts reduce the vulnerability of uninvaded habitats to colonization (73). While this concept
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parallels existing epidemiological emphasis on prevention versus control, its successful
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application requires deeper insights into whether a parasite community is dispersal-limited,
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niche-based, or is random in its assembly (Fig. 2). This approach can be used to strengthen
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current methods of infectious disease management across the gamut of multi-host parasites,
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multi-symbiont communities, and infection heterogeneities across scales (Fig. 3).
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Managing multi-host parasites
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A current pressing question is how ongoing changes in biodiversity will affect the spread and
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severity of infectious diseases (74). When diverse communities also support species that interfere
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with transmission, such as the presence of low susceptibility hosts, predators, or symbionts,
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community structure can be manipulated defensively to manage infections by limiting niche
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suitability (37). For example, zooprophylaxis (in which livestock are used as bait to divert blood-
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feeding arthropod vectors away from people) has been proposed as a control strategy for vector-
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borne diseases for over a century, but has had limited success in some settings because increased
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livestock density can also increase vector abundance. However, recent models on malaria and
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zoonotic cutaneous leishmaniasis indicate that carefully chosen livestock densities coupled with
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insecticide treatment can effectively reduce parasite transmission to humans (75, 76). Similar
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approaches, such as intercropping and crop rotation, have been used successfully to reduce plant
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pests and parasites in agricultural systems (77). While evidence for such dilution effects
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continues to grow (78), the degree to which biodiversity will regulate infection by a particular
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parasite depends on the degree to which host assembly is deterministic, whether the parasite is
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niche- or dispersal-limited, and how increases in richness affect host and vector abundance.
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Managing host communities is also crucial to mitigating the risk of spillover events from
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animal reservoirs to humans. To minimize the risks of spillover, there are several potential
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offensive and defensive approaches, the choice of which will depend on the specific biology of
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the hosts and vectors involved. The first option is to reduce infection in reservoir hosts. For
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instance, vaccine baits have successfully eliminated rabies from several European countries
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through their protective effects on non-human hosts (79). The second approach is to limit
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contacts between wildlife and humans, for example by reducing bushmeat consumption and its
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potential to introduce novel infections (80). In West Africa, for example, increasing the use of
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alternative protein sources such as marine fish could relieve pressure on the bushmeat trade (81).
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Such approaches require tight coordination between many parties, including medical scientists,
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anthropologists and governments. Similarly, the use of transmission barriers can help to limit
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contact between wildlife reservoirs and domestic animals (82, 83). The third approach is to
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reduce the probability of infection when contact is unavoidable or unpredictable. Ongoing yet
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unpredictable spillovers of dengue viruses from non-human primates, for instance, complicate
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the control of human disease in Southeast Asia and Africa. One approach to control such
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infections is through the implementation of cross-reactive vaccines, which are currently under
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development (84). When vaccines are not available yet, as is the case for Ebola virus, reducing
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human-human transmission through contact tracing and subsequent quarantine and treatment can
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help to limit epidemic spread (85).
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Managing symbiont communities
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Interactions among co-infecting parasites or symbionts can also be used as niche-based
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management tools. For example, treating patients suffering from lymphatic filariasis with the
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antibiotic doxycycline eliminates essential symbiotic bacteria required by filarial worms,
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ultimately leading to worm sterility and death (86). Restoration or augmentation of the microbial
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community within the host can also provide protection against parasite invasion. For example,
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transferring human-microbial communities by fecal transplants often leads to clinical resolution
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of intestinal pathology associated with Clostridium difficile infection (87). Finally, interactions
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among co-infecting parasites, parasite strains, or other symbionts can be manipulated to reduce
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the spread of disease-causing organisms. Long-lived parasites, such as helminths, may
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exacerbate disease caused by co-infecting parasites, leading to calls to incorporate de-worming
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to improve management of HIV, malaria and TB (16, 88). In other cases, antagonistic
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interactions between parasites or other symbionts may be used to benefit the host. For instance,
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trials are under development to reduce the vector competence of mosquitoes by infecting them
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with the bacterium Wolbachia, which inhibits Dengue virus and filarial worm survival and
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transmission through a combination of immune activation, competition for cellular components,
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and shortened mosquito lifespan (89, 90). These examples emphasize the importance of
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understanding and predicting the outcome of multiple infections, for which community ecology
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approaches focused on parasite traits and resource use have already offered added insights (91).
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Heterogeneity and scale
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The disproportionate roles of particular locations, particular host species, and particular
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individual hosts in driving epidemics or epizootics raises the tantalizing promise of highly
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efficient targeted control and treatment (22, 48, 50). In the Serengeti, for example, where rabies
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can infect up to 12 carnivore species, domestic dogs are responsible for more than 70 percent of
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transmission events to humans (92). Annual vaccination of 60 percent of dogs is projected to
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control the virus, a target that is logistically and economically feasible (93). During the recent
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Ebola epidemic in West Africa, close contact between deceased patients and family or friends
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during traditional burials functioned as superspreading events (94), and implementation of
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‘sanitary burials’ that reduced such contacts helped curb the epidemic. Thus, targeting
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superspreading hosts or events is feasible when transmission heterogeneities are deterministic
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and can be linked to measurable traits or characteristics.
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Ultimately, the efficacy of offensive and defensive approaches will depend on whether
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the scale of application is local or regional, the transmission and dispersal characteristics of the
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parasite involved, and the point in the epidemic when the intervention is initiated and how many
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individuals have already been infected (72). Defensive, niche-based management strategies,
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ranging from vaccination and prophylaxis to ecological competition by probiotic symbionts, are
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more likely to be effective when parasite dispersal is high, for parasites with high or
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unpredictable propagule pressure, and for epidemics already underway (Fig. 3). In contrast,
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offensive strategies that focus on reducing dispersal are more likely to succeed at community and
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regional scales than at individual and population scales, because parasite dispersal between
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individuals within a host population is often harder to control than dispersal between sites. For
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instance, established populations of the Asian tiger mosquito, Aedes albopictus, recently linked
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to a large outbreak of the viral disease chikungunya on the Indian Ocean Island La Réunion (95),
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are almost impossible to eliminate; however, because most introductions of this vector have
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occurred through the shipment of used tires, focused efforts to limit this trade offer the best
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potential for containing future spread of the vector (95).
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Outlook
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While the disciplines of epidemiology and community ecology have developed largely
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independently from one another, the multi-species nature of many contemporary disease threats
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demands a community-scale approach to complement more traditional biomedical treatments.
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Community ecology offers a theoretical framework and the analytical tools to move beyond the
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historical emphasis on particular host-parasite interactions and consider the full suite of species
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that influence infection dynamics. We have emphasized approaches from community ecology
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that can advance our ability to manage infections by: (1) identifying the factors that govern the
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structure and dynamics of communities composed of multiple hosts, vectors, and symbionts, (2)
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identifying the drivers of heterogeneity, and (3) understanding how processes and patterns link
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across multiple scales of biological organization. For many emerging infections, complete
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eradication is unlikely to be successful, but a broader understanding of the ecological
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communities in which host-parasite interactions are embedded will facilitate more effective
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management.
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Transforming this broader understanding into practical disease management requires tight
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integration of surveillance, community ecology analysis, and public health implementation (96).
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Ongoing technological advances are rapidly overcoming previous barriers in data quality and
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quantity, highlighting emerging opportunities to incorporate approaches from community
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ecology into existing disease research and evaluate the factors driving the structure and dynamics
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of natural disease systems. Combining analyses of these high-resolution data with modeling
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approaches and large-scale manipulations of host-parasite interactions – similar to the
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foundational experiments from community ecology (66) – offer among the best opportunities for
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developing a deeper understanding of the processes underlying disease emergence and control.
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To date there have been some practical successes that follow this broad approach. For example,
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following the observation of five dead howler monkeys - a key host for yellow fever virus - a
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collaborative effort between the USAID PREDICT program and the Bolivian government led to
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rapid implementation of human vaccination and mosquito control in the affected area (97).
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Similarly, increased use of buffer zones between fruit trees and livestock housing has been
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effectively used in Malaysia to reduce Nipah virus transmission into pigs and the risks of human
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outbreaks (82), while electrified fences in Kruger National Park have helped limit contact
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between bovine TB-infected wildlife and cattle in surrounding areas (83). Such scenarios
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demonstrate how a broader appreciation for the epidemiological links among humans, domestic
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animals, and wildlife can result in more effective control of disease risk in ecological
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communities.
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References and Notes
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Boxes and Figures
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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.
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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.
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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)
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Parasite community assembly is also influenced by ecological drift (stochasticity), particularly
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when colonizing populations are small or the outcome of parasite interactions depends on their
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order of arrival (‘priority effects’). As a result, parasite communities can appear random with
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respect to host species or type, even if strongly affected by species interactions. (C) High rates of
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dispersal can swamp niche effects and overwhelm stochasticity, resulting in more similar
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parasite communities across hosts, regardless of host species. For simplicity, no feedback loops
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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.
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Levels of ecological organization are shown in the middle, and colored arrows indicate the
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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.
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Grey shaded arrows indicate the relative importance of offensive strategies (preventing parasite
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dispersal) and defensive strategies (preventing parasite establishment), with darker shades
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reflecting greater importance. (B) Management strategies focused on reducing spillover from
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wildlife to humans (zoonosis) and from humans to wildlife (anthronosis or reverse zoonosis).
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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
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instance, vaccine baits have been successfully used to eliminate rabies from several European
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countries (79). Second, contact rates can be reduced between humans and wild animals (8); for
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example, limiting the proximity between humans and wildlife can reduce spillover of human
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illnesses, such as measles, tuberculosis, and MRSA, to wildlife. Third, zoonotic risk can be
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reduced by lowering the probability of infection when contact is unavoidable or unpredictable.
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For instance, some human Dengue vaccine candidates provide cross-protection against sylvatic
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Dengue viruses, which naturally circulate in non-human primates (84). Finally, when spillover
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does occur, regional control strategies – including isolating infected populations, dispatching
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medical personnel and aid, and enhanced border control – can be used to prevent disease
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transmission across borders.
680
26
681
Box 1: The role of simple theory in disease ecology and its extension to complex
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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
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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.
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parasites often face a diverse community of potential host species, which differ in abundance,
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susceptibility and infectiousness. Simple extensions of basic disease ecology theory can
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determine the conditions under which one host species amplifies or dilutes infection risk for
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other species in the community. For directly transmitted parasites, or even those transmitted via
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infective stages in the environment, theory shows that the parasite's overall basic reproductive
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number among the available host community (R0,TOT) can simply be proportional to the sum of
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the R0 for each host species alone, provided there is equal mixing within and between host
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species (although other relationships between the individual-level and community-level R0
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values may occur if mixing is not equal) (10, 100). Hence, there is a clear connection between
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this more complex scenario and the classical single-host theory.
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This theory can be extended further for vector-borne parasites, which become
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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
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the parasite but essential for tick reproduction (Box 1, Fig. 1). Here there are three possible
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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
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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
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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)