An equilibrium theory signature in the island biogeography of human parasites and pathogens

Introduction

Infectious diseases remain one of the chief causes of human morbidity and mortality world-wide, especially among the young and the poor (Lozano et al., 2012). Understanding the drivers of human pathogen diversity, a key predictor of the prevalence of infectious disease, is a critical challenge for the 21st century (Dunn et al., 2010). The diversity of infectious agents and the burden of disease vary dramatically across the globe, as they have throughout human history (Wolfe et al., 2007; Dunn et al., 2010). This disease burden exerts a profound effect on the economic fortunes of entire nations and world regions (Bloom & Sachs, 1998; Bonds et al., 2012). However, our understanding of the biogeography of human disease is surprisingly limited. Fewer than 10 infectious diseases have been mapped comprehensively (Hay et al., 2013), and we know less about the distributions of many human parasites and pathogens than we do about those of most rare birds (Just et al., 2014). As human populations grow and geographically change with urbanization and migration, exposing populations to novel social and ecological environments, there is an increasing need for first-order predictions to guide policy and future research.

Human parasites and pathogens interact both with their human hosts and the broader environment, so their distributions should be a function of general ecological factors as well as of the specific ecology of Homo sapiens. Indeed, despite our sense of the ubiquity of microbes, ecology still drives the world-wide distribution of human disease, the inspiration for the eponymous Baas-Becking hypothesis: ‘Everything is everywhere, but the environment selects' (Baas-Becking, 1934). As with species generally, the tropics have many more disease-causing species (Guernier et al., 2004; Jones et al., 2008; Peterson, 2008), and Earth can be divided into biogeographical human-disease regions (Just et al., 2014). Considered broadly, our parasites and pathogens display patterns characteristic of animals and plants generally (Guernier et al., 2004). At the same time, pestilence follows patterns of human dispersal and interaction. As anatomically modern humans migrated to new environments, such as from Africa to Eurasia and then to the Americas, our ancestors spread some pathogens, shed others and acquired new ones along the way (Burnside et al., 2012). Historic and continuing changes in human population density, promoted by agriculture and then by industrialization, engendered and supported the ‘crowd-epidemic diseases’, such as seasonal influenza, measles and pertussis, that afflict urban residents (Bjørnstad & Harvill, 2005; Furuse et al., 2010). With globalization, increasing travel, migration and trade pathogens and parasites specific to humans have spread world-wide, though those with animals as their main reservoir and humans as secondary hosts remain more localized (Smith et al., 2007). Illuminating the processes driving such large-scale epidemiological patterns is a growing focus of disease ecology (Guernier et al., 2004; Dunn et al., 2010; Bonds et al., 2012).

A proven avenue for exploring the influence of spatial ecological and evolutionary processes is to study patterns of biodiversity on islands. As Darwin argued, islands form natural laboratories where processes can be observed that are too complex to track on land masses (Darwin, 1859). MacArthur and Wilson formalized this insight in their influential ‘equilibrium theory of island biogeography’ (MacArthur & Wilson, 1967). According to this theory, the number of species living on an island represents a dynamic equilibrium between species arriving from elsewhere (immigration) and those dying out some time after they arrive (extinction). The immigration rate declines with distance to the nearest mainland, the source pool, while the extinction rate declines with island area, because larger islands can support larger populations with correspondingly lower probabilities of dying out. Once an island has reached ecological equilibrium, invasions will balance extinctions and the number of species will remain unchanged even though their composition may vary over time. The equilibrium theory of island biogeography has successfully explained a range of patterns of insular plant and animal species (Lomolino et al., 2010) as well as of microbes with animal hosts (Bell et al., 2005; Orrock et al., 2011; Svensson-Coelho & Ricklefs, 2011), but its applicability to human pathogens is unclear. Previous research supports the existence of biogeographical patterns in microbes (e.g. Martiny et al., 2006; Hanson et al., 2012), but these studies were limited to free-living microbial taxa and not focused on host-associated pathogenic species. Recent, more-limited work on historical human populations supports the theorized effect of island size on the diversity of vector-borne pathogens (Cashdan, 2014), though the influence of distance and the effect of modern industrial lifestyles, with their enhanced mobility and access to medicine and public health, is less clear.

In this study, we use the Global Infectious Disease and Epidemiology Online Network Database (GIDEON) to examine whether the distribution of nearly 300 human pathogens occurring on different islands conforms to the general predictions of island biogeography theory, specifically that pathogen richness is a positive function of island size and a negative function of distance to the nearest mainland.

Materials and Methods

Data collection

Analyses were based on a subset of data extracted and compiled from GIDEON (http://www.gideononline.com/). GIDEON provides clinical, geographical and epidemiological information on 332 unique viruses, bacteria, fungi, protozoa and helminths infecting humans in each of the 222 countries and administrative territories of the world. The database is updated regularly using publications from Medline based on a list of keywords and search information published by national health ministers, the World Health Organization (WHO) and the US Centers for Disease Control and Prevention (CDC). As such, GIDEON is the most up to date global database available for human infectious disease.

For simplicity, we use the term ‘pathogens’ in this paper to cover both pathogens and parasites, and consider disease names (e.g. measles) as synonymous with the infectious agents that cause them.

Following Guernier et al. (2004) and Smith et al. (2007), we excluded pathogens causing infectious diseases that did not meet the following three criteria: (1) those with multiple aetiological origins, (2) those with major uncertainties surrounding national presence/absence, and (3) vector- and reservoir-borne pathogens with imprecise information about their hosts. The resulting database included 271 pathogens: 85 viruses, 87 bacteria, 15 fungi, 64 helminths and 20 protozoa.

We categorized pathogens three ways to assess the importance of different ecological and evolutionary processes: by host association, by transmission mode and by taxonomy. We assigned host associations following Smith et al. (2007) as: (1) human-specific pathogens (n = 83), which circulate exclusively in the human reservoir and are transmitted from person to person and hence are contagious, e.g. measles; (2) zoonotic pathogens (n = 152), which develop, mature and reproduce entirely in non-human hosts but can still infect humans, who are then dead-end hosts, e.g. rabies; and (3) multi-host pathogens (n = 36), which can use both human and non-human hosts to complete their life-cycle, e.g. the Ebola virus. We assigned pathogen transmission mode as follows: pathogens that spread through an arthropod vector (n = 82) versus those not transmitted through a vector (n = 189). Finally, we categorized pathogens by major taxonomic group: viruses, bacteria, fungi, protozoa and helminths (including both helminth worms and nematodes).

Our geographical choices are driven by island biogeography theory. From 222 administrative territories recorded in GIDEON, we extracted data from 66 island countries and territories (Fig. 1a), the largest being Madagascar and the smallest Tokelau (see Appendix S1 in Supporting Information). Inclusion or exclusion of islands was driven by the completeness of information for a set of geographical, socioeconomic and demographic indicators based on previous, complementary work (Guégan & Broutin, 2009).

Since the inclusion of all islands in this sample could introduce confounding effects, such as those related to latitude, and because the equilibrium theory was originally elaborated for a group of islands within an archipelago, we extracted from the whole island dataset two regional island subsets, one for Caribbean islands (n = 24, Fig. 1b) and one for Pacific islands (n = 21, Fig. 1c). Territorial surface areas (in km²) and total human population size were extracted from the 2010 World Factbook, published by the US Central Intelligence Agency and updated yearly. ArcGIS software, version 9.3.1 (ESRI, Redlands, CA, USA) was used to compute the centroid of each island and the distance in kilometres from that centroid to the nearest mainland shoreline.

Results

Species richness relationships with area and distance in the entire sample

Our findings for the entire sample of island countries and territories supported predictions from the equilibrium theory of island biogeography, though the effect of area on pathogen diversity was much more pronounced than that of distance. Figure 2 presents the island SR plotted against, respectively, surface area (Fig. 2a) and distance to the mainland (Fig. 2b). Larger islands support more species of pathogens, as shown in Fig. 2 (a) [y = (1.695 × 10⁻²)x + 2.022, P < 10⁻³]. Island surface area explained more than 40% of the total variance of pathogen SR (R²_adj = 0.407). In turn, more-isolated islands tended to support fewer pathogen species, as shown in Fig. 2 (b) [y = (−6.394 × 10⁻⁶)x + 2.087, P = 0.014], though this relationship explains less than 10% of the total variance of SR (R²_adj = 0.0766).

Relationships between SR and host requirement, transmission pathway and taxonomy

Across all pathogen subcategories, SR increased with island surface area and decreased with distance to the nearest mainland. However, as Fig. 3 shows, the extent of these relationships, as indicated by differences among regression slopes, is driven by zoonotic status, vectorial transmission and protozoan and helminthian taxonomy. Pathogens that infect humans obligately, those that do not require a vector for transmission and those that are relatively small (viruses, bacteria) are much less affected by island biogeography.

The positive relationship between SR and surface area was significant for every pathogen subcategory (each P < 10⁻³; Table 1, the strength of this relationship varied significantly across pathogen host-requirement categories (slope coefficients: human only pathogens, 5.768 × 10⁻³; multi-host pathogens, 1.210 × 10⁻²; zoonotic pathogens: 3.788 × 10⁻²; ANCOVA P < 10⁻³), transmission pathways (slope coefficients: directly transmitted pathogens, 0.0132; vector-borne pathogens, 0.0469; ANCOVA P < 10^–3), and taxonomic categories (slope coefficients: bacteria, 8.932 × 10⁻³; viruses, 1.486 × 10⁻²; fungi, 1.522 × 10⁻²; protozoa, 3.733 × 10⁻²; helminths, 2.416 × 10⁻²; ANCOVA P < 10⁻³).

Table 1. Results of univariate linear regressions of log number of pathogen species classified by host requirement, transmission pathway and taxonomy, as functions of (a) island surface area (log) and (b) distance to mainland for the total island sample (n = 66)

	(a) Island surface area (log km2)					(b) Distance to mainland (km)
Pathogen species richness classified by:	Slope (×10−2)	Intercept	R2adj	P	ANCOVA P	Slope (×10−6)	Intercept	R2adj	P	ANCOVA P
Host requirement					<10−3					0.354
Multi-host	1.21	1.25	0.178	<10−3		–5.00	1.29	0.032	0.081
Zoonotic	3.79	1.42	0.449	<10−3		–10.99	1.56	0.043	0.051
Human only	0.58	1.80	0.185	<10−3		–4.10	1.82	0.13	0.002
Transmission pathway					<10−3					0.034
Vector-borne	4.69	0.94	0.277	<10−3		–23.10	1.13	0.089	0.009
Directly transmitted	1.32	1.99	0.392	<10−3		–4.38	2.04	0.051	0.038
Taxonomy					<10−3					0.343
Bacteria	0.89	1.70	0.187	<10−3		–4.62	1.73	0.063	0.024
Viruses	1.49	1.45	0.337	<10−3		–4.72	1.50	0.037	0.065
Fungi	1.52	0.81	0.241	<10−3		–5.79	0.87	0.039	0.061
Protozoa	3.73	1.12	0.363	<10−3		–13.88	1.02	0.154	0.001
Helminths	2.42	0.92	0.329	<10−3		–9.95	1.26	0.025	0.110

• The linear relationship between species richness (SR) and island surface area expressed in logarithmic space corresponds to the classical power model of the species–area relationship, generally expressed as SR = cA^z, where A is the surface area, c is the intercept and z in the linear coefficient, or slope.

The negative relationship between SR and distance to the nearest mainland was significant or at the limit of significance for nine of the ten categories we considered (for five categories P < 0.05; for four categories P < 0.10; Table 1). The strength of this relationship varied significantly among pathogen transmission pathway categories (slope coefficients for directly transmitted and vector-borne pathogens, respectively, −4.380 × 10⁻⁶ and −2.310 × 10⁻⁶; ANCOVA P = 0.0343).

Discussion

We have shown here that the distribution of known human pathogens on islands follows the main predictions of MacArthur and Wilson's equilibrium theory of island biogeography (MacArthur & Wilson, 1967): pathogen species richness increases with island area and decreases with distance to the nearest mainland. However, the relative influence of area is much greater than that of isolation, and the extent and strength of the associations vary by host requirement, transmission pathway and pathogen taxonomy. Importantly, pathogens whose primary hosts are not humans are more strongly affected by island biogeography than those that primarily afflict people.

A limitation of this study is the relatively small sample size, an inherent constraint of focusing on a relatively small subset of the larger GIDEON dataset. The resulting lack of statistical power did not allow us to account simultaneously for different categorical factors or to take into account other factors previously identified as important drivers of pathogen richness, such as climate (Guernier et al., 2004; Dunn et al., 2010). However, our purpose was not to identify and assess the relative influence of a large set of variables but rather to test how well an influential biogeographical theory describes a pattern of current human ecology. Conducting a complementary analysis on regional sub-datasets (Caribbean and Pacific islands) was a way to control for shared characteristics of islands from the larger sample, such as latitude and regional biotic influences. The fact that the results of these regional analyses were similar to those for the whole dataset supports the validity of the relationships we found.

Another limitation is that GIDEON is an evidence-based database, so the data could, potentially, reflect a reporting bias. Indeed, wider sampling or research efforts on larger or less-isolated islands could contribute to the results described here. Hypothetically, although such a reporting bias for this island dataset could influence our findings, it is unlikely that this bias would produce the patterns we observed across pathogen categories. Furthermore, healthcare expenditure is a poor predictor of human pathogen SR at the country level (Dunn et al., 2010), even if it does predict the prevalence of infectious pathogens. Thus, our results are likely to be independent of any reporting effect.

According to the equilibrium theory of island biogeography, the positive relationship we found between human pathogen species richness and island area is due to lower extinction rates on larger islands. Larger islands contain larger habitat areas and a likelihood of greater habitat diversity. For human pathogens, larger habitat areas should support more host individuals, including more humans, and greater habitat diversity should support more species of alternative hosts. Indeed, nations with more people and more species of birds and mammals support more species of human pathogens (Dunn et al., 2010), and habitat diversity drives the diversity of bacterial assemblages generally (Nemergut et al., 2011). So the patterns we observed for human pathogens could simply mirror: (1) a species–human population relationship, in which the human population serves as the ‘area’ that pathogen species occupies, and/or (2) the species–area relationships for alternative insular host species.

However, the much greater effect of area on the species richness of zoonotics than on multi-host and human-only pathogens (Fig. 2a) suggests a strong mechanistic role for alternative insular hosts. In short, island animals for which pathogens are primary hosts help drive the island biogeography of human disease. This is not surprising, because these animals are much more restricted in their ability to travel than are modern humans. This finding also corresponds with the strong relationship between pathogen diversity and bird and mammal species diversity (Dunn et al., 2010) and the findings of a broader analysis that included continental nations (Smith et al., 2007).

Greater immigration rates would also be likely moderate the effect of area on human-only pathogens through the rescue effect, a refinement of MacArthur and Wilson theory. The rescue effect (Brown & Kodric-Brown, 1977) is the effect of immigration on extinction, which in the original theory was solely a function of island size. By continually bringing pathogens with us to islands when we migrate or travel, we ‘rescue’ some pathogen species that might otherwise die out on smaller islands (i.e. when the number of susceptible individuals drops below a threshold necessary to sustain the disease – the critical community size) (Rohani et al., 1999).

Together, these features specific to human pathogens differentiate island microbes from other insular taxa. A positive relationship between island size and bacterial diversity holds in engineered systems (Van der Gast et al., 2005), supporting the generality of this biogeographical pattern and the importance of species–area/volume effects among microbes generally (Green & Bohannan, 2006). Yet mass-related limitations on active dispersal and exceptional rates of diversification (Martiny et al., 2006) may explain the exceptionally low effect of area we observed: the value of the coefficient linking species richness and island surface (the z-value) for insular human parasites and pathogens was about an order of magnitude lower than those of insular macroorganisms (Table 3).

The negative relationship we found between pathogen SR and distance to the mainland is a function of varying immigration rates in MacArthur and Wilson's theory. Several factors may explain the relative weakness we found in the influence of isolation versus that of area. In the original theory, ‘distance’ is the distance separating different islands to the same continental shore, viewed as the source of the same set of species. However, species considered here are pathogens of Homo sapiens, whose capacity for large-scale movement has increased continuously, especially during the past five centuries (Smith & Guégan, 2010). This increase in connectedness, which has profoundly lessened effective isolation, is likely to explain the much smaller effect of distance. One way to test this idea would be to explore the relationship between island pathogen diversity and transport connectedness (Colizza et al., 2006).

The finding of a greater role for area than for human host population and distance can be seen as supporting the Baas-Becking hypothesis, which posits that, regarding microbes, ‘everything is everywhere, but the environment selects’ (Baas-Becking, 1934) and a corresponding non-stochastic view of microbial community assembly (Barberán et al., 2014). In the case of zoonotics, the environment of interest is composed of non-human hosts and their habitats are not everywhere.

This work has several practical, broad-scale implications. The fact that our travel and trade swamps isolation so profoundly means there that few islands will remain disease free. However, the importance of area in supporting populations of vectors and alternative hosts means that the proportion of island diseases that are zoonotic and vector-transmitted will tend to decline with decreasing island size, with implications for public health management efforts. These implications should apply to current islands as well as to those, given time for equilibration, that are created by, or whose area or isolation is altered by, rising sea levels. However, the conclusion that reducing or fragmenting habitats is a viable public health strategy would misinterpret the broader lessons of ecology in the Anthropocene. The incursion of human populations into natural habitats is already associated with outbreaks and emergence of zoonotic diseases, and habitat loss would suppress biodiversity more broadly and have a disproportionate impact on larger taxa such as mammals. As global biodiversity benefits human health and well-being in many ways, such a strategy would harm more than help.

Our results demonstrate how classic island biogeography theory applies to human pathogens, and our findings support the spirit of theoretical insight as much as the substance. Even if infectious diseases have been widely globalized because of large-scale human movements, area and isolation still affect macroscopic disease patterns. And the ways in which the results seem to show weak support – in the relative effect of isolation on human-only pathogens – highlights the importance of the underlying process, immigration, which is so strongly constrained by isolation. Globalization effectively increases pathogen immigration rates, reducing the historic barrier of isolation. Just as humans both follow and flout ecological patterns common to species generally (Burnside et al., 2012), so do the parasites and pathogens that afflict us. And just as some aspects of biogeography are common to life generally, others may be unique to microbes. As it did for our understanding of assemblages of plant and animal communities, we hope that this test of ‘equilibrium theory’ will be a stepping stone in the understanding of causal drivers behind global trends in human infectious disease and in the broader quest to understand the geography of life.