Malaria arising from infection with Plasmodium falciparum is a major cause of morbidity and mortality in tropical and sub-tropical regions of the world [1]. The difficulty in controlling this devastating disease has been due in part to high levels of genetic diversity of P. falciparum, allowing the rapid evolution and dissemination of advantageous traits such as drug resistance and antigenic variability. Malaria control would be more effective if the target parasite populations could be surveyed before an intervention to determine the extent of (i) genetic diversity, as a predictor of the populations' resilience to interventions; (ii) linkage disequilibrium, to understand the potential for multilocus haplotypes to spread through the region; and (iii) population structure, to map the distribution of diversity over geographic space and thus infer patterns of parasite migration. Population genetic surveys are therefore an essential preliminary step in designing the most appropriate and effective malaria control measures and as a baseline upon which to monitor their impact.

The worldwide population genetic structure of P. falciparum, as defined by multilocus genotyping, shows a general pattern of increasing genetic diversity, but decreasing linkage disequilibrium (LD) and population differentiation in association with the parasite transmission intensity (Americas < Asia Pacific <Africa) [2, 3]. This original concept is being constantly updated, with new studies being used to describe the various patterns found locally within each continent. In the Americas, parasite populations continue to be characterized by low diversity, high levels of LD and strong population structure independent of geographic distance [4]. In Asia, more extensive studies have demonstrated higher levels of diversity than previously recognized and a lack of LD [5]. Moderate to high levels of population structure among countries of mainland Asia [6], and locations within Malaysia (Sumatra) and the Philippine islands [7, 8] have been reported. In Africa, population structure, low levels of genetic diversity and significant LD have been described in the urban populations of Senegal, Niger and the Republic of Djibouti [9]. Significant LD has also been found in regions of high transmission and diversity in Senegal and the Republic of Congo [10, 11]. The variable results observed are likely the result of inherent features to each geographic region such as the genetics and movement of human and anopheline hosts and biogeographical features that may interrupt gene flow, as well as the history of malaria transmission [2, 12] and local malaria control efforts [7]. This emphasizes the importance of investigating the parasite population genetics within each region of interest, particularly now malaria elimination is back on the agenda in many countries.

The epidemiology of malaria in the South West Pacific nation of Papua New Guinea is highly variable. Malaria transmission is confined to the coastal and lowland zones where intense perennial transmission of the four major human malaria species (P. falciparum, Plasmodium vivax, Plasmodium malariae and Plasmodium ovale) occurs. Whereas, in the highlands, Plasmodium spp. infection is present but is mostly due to sporadic epidemic transmission [13]. In the endemic regions, the degree of P. falciparum transmission is high but variable among different regions, villages and even clusters of houses within villages [14], with entomological inoculation rates ranging from 0.15 - 1.44 infective bites/person/night [15–18]. Accordingly, the prevalence of multiple infections also varies greatly within this region, ranging from 26 to 50% [2, 19–21], thus creating a range of opportunities for recombination between different genomes and the further generation of genetic diversity. This diverse micro-epidemiology has been attributed to differing patterns of host behaviour [22], nutrition [23–26], mosquito control [14, 27], the wide range of vectors present [16, 27, 28] and more recently, bed net usage [20]. Variable patterns of parasite genetic diversity may result from, or underlie this mosaic pattern of malaria epidemiology. In addition, the biogeography of the country including mountains, thick forests and large rivers with limited transport have resulted in the isolation of human populations, as evidenced by the existence of more than 800 local languages [29] and different frequencies of human genetic polymorphisms that protect against malaria in different provinces [30]. The human diversity is matched by a highly diverse vector fauna with at least seven members of the Anopheles punctulatus complex and several minor species contributing to the transmission of malaria [13]. These vector species differ both in geographical distribution [27, 31, 32] and biting behaviour [33] and the major mosquito vector in the country, Anopheles farauti s.l. is known to fly only short distances (< 2 km). For these reasons, it is possible that gene flow among different parasite subpopulations of Papua New Guinea is limited. The presence of local parasite population structure has been suggested by differing seroprevalence to a P. falciparum antigen (S-antigen), even among closely spaced villages [34]. Consequently, it will be essential to sample more than one parasite population to explore the population structure of P. falciparum in Papua New Guinea. To date, there has only been one other study investigating the population biology of P. falciparum in Papua New Guinea by multilocus genotyping [2]. Using putatively neutral microsatellite markers, this study reported high levels of genetic diversity, a lack of LD and limited genetic differentiation between two neighbouring villages on the coast of Madang Province [2].

Given the locally variable epidemiology of malaria in Papua New Guinea [14], knowledge of the population genetics at different spatial scales is essential for decision-making for targeting P. falciparum populations for malaria control, managing the spread of drug resistance, developing approaches for vaccine design and eventually, elimination programs. The aim of this study was to describe the population genetics of P. falciparum sampled from thirteen villages spread over four distant catchments of East Sepik and Madang Provinces. Multilocus haplotypes were defined by genotyping a validated panel of microsatellite markers [35] and the extent and distribution of genetic diversity within and among parasite populations was measured. The results have important implications for programs targeted at controlling and eliminating this major human pathogen.

This is the most extensive study to date investigating the genetic structure of P. falciparum populations of Papua New Guinea. Included in the study were the parasite populations of thirteen villages (or village-pairs) distributed over two inland and two coastal catchment areas in the north of the country where malaria research and control efforts are focused. A previous analysis of the same set of microsatellite loci in two coastal villages (Buksak and Mebat) approximately 80 km apart in nearby areas of Madang Province reported a high degree of genetic diversity (H_e = 0.62 - 0.65), a lack of significant LD (I _S ^A = 0.0055- 0.0073; P > 0.05) and minimal differentiation between the two populations (F_ST = 0.015) [2]. We have similarly identified high levels of diversity and a lack of LD, however by surveying many more villages over a larger area, we have discovered a wider range of diversity than previously shown (H_e = 0.64-0.77) and that parasite populations are heterogeneous with moderate to very high population structure detected throughout the study area (F_ST = 0.05 - 0.33; P < 0.01). Differences between the study of Anderson et al[2] and the current findings are consistent with a variety of P. falciparum population structures throughout Papua New Guinea.

Levels of diversity are an indication of the fitness of the parasite population and thus how difficult it may be to target with drugs or vaccines. The diversity among catchments was high but also variable, with the inland populations of Wosera and Utu having lower levels of allelic richness and heterozygosity than the coastal populations of Malala and Mugil. The lack of association between the molecular epidemiological correlates of transmission and diversity in Papua New Guinea suggest that a number of factors influence the population genetics of P. falciparum in the region, these are discussed below. Given the potential difficulty in controlling diverse parasites, such knowledge has important practical implications for malaria control across the country.

As Papua New Guinean parasite populations had high levels of genetic diversity it was not surprising to find a lack of significant LD in all four catchments studied, while the LD found for the total dataset can be explained by the Wahlund effect due to the observed population structure [52]. Within each parasite population a large proportion of multiple infections was found and therefore cross-fertilisation and recombination between distinct parasite genomes would be expected to maintain random associations among loci. LD has important implications for the spread of multilocus drug resistance haplotypes, with high levels of inbreeding increasing their dispersal. In Papua New Guinea, the lack of LD combined with the geographic population structure would be unlikely to facilitate such events.

The population structure between the inland (Wosera and Utu) and coastal populations (Malala and Mugil) indicated the existence of barriers to gene flow and thus parasite migration, and other possible influences on population structure such as natural selection and genetic drift within each catchment. Although only ~50 km from the provincial capital of Madang town, it takes several hours to travel to the remote Utu village with only one road entering and leaving and there is no direct route of travel (road or air) between Madang and the East Sepik Provinces. Whereas, the lower extent of population structure between Malala and Mugil, and small amount of mixing between these two populations indicated in the cluster and network analyses probably reflects their direct connection via the Pacific Highway. Despite this direct route of possible gene flow between Malala and Mugil, the population structure was significant. Migration of diverse parasites into these locations via human movement may partially explain this observation. For Malala, there is a boarding school with students attending from across Madang and East Sepik Provinces. Lower levels of differentiation among catchments occurred between Wosera and Malala so it is plausible that some gene flow occurs between these locations via movement of students and their guardians. In Mugil, there is constant movement of people ferrying to and from the well-populated Karkar Island (17 km of the coast), however the lack of samples from this location makes this speculation difficult to confirm. The genetic differentiation and cluster analyses also indicated that population structure occurs on a local scale (< 20 km). In particular, in the Malala area the village of Amiten/Susure which is located ~6 km inland was found to be genetically distinct from Malala/Suraten and Wakorma which flank the school. In fact, these analyses suggested that the Amiten/Susure population was more similar to parasites from the Mugil villages, suggesting that they may represent the "true Madang coast" population. In Mugil and Wosera, some villages were also differentiated. Here, the low but significant F _ST values can be explained by small sample sizes for all except for that between Matukar/Bunu and Karkum (Mugil), and Nindigo and Gwinyingi (Wosera). However, the cluster and network analyses did not suggest any geographic population structure within catchments other than Malala. An isolation-by-distance model did not explain the observed geographic population structure, suggesting that there is a non-continuous distribution of diversity in Papua New Guinea. This fragmented population structure may be explained by movement of the human host with a lack of transport between catchments combined with higher rates of migration into coastal catchments as described above.

There are other possible explanations for the patterns of population structure within Papua New Guinea. People from different catchments belong to different language groups [29], indicating historical separation of human populations and presumably the parasites infecting them. A different prevalence of genetic polymorphisms that protect against malaria between Madang and the East Sepik [30] might have also provided unique selective pressures for the respective parasite populations. In addition, a possible role for the anopheline vector in shaping the observed population genetic structure of P. falciparum in Papua New Guinea cannot be ignored. At least six distinct anophelene species transmit malaria in the region. In Madang Province, Anopheles farauti 4 is predominant in the inland villages such as Utu and Amiten/Susure whereas A. faurauti 1 is more common in villages that are proximal to the coast [31, 32]. In the Wosera, Anopheles koliensis and Anopheles punctulatis are the predominant vectors [27]. The population structure observed was consistent with these vector distributions. In Africa, investigators have found no evidence of P. falciparum population structure between two co-existing vectors, Anopheles gambiae and Anopheles funestus[53] suggesting that transmission by these vector species is not a strong barrier to gene flow. In Papua New Guinea though, the vector species distribution is highly heterogeneous with a limited overlap [27, 31, 32] so the ability of the different species to transmit allopatric parasites would have to be tested. Other factors that influence transmission intensity such as the use of bed nets [20] may also impact on the overall population structure. Whatever the explanation, the population structure of P. falciparum in Papua New Guinea is likely the result of a combination of factors, including the limited movement of both human and mosquito hosts, in addition to the greater accessibility of the coast in comparison to the inland populations. The public health implication for these findings is that parasite populations that might be assumed to be similar for development of malaria control strategies, such as vaccines, in fact are genetically distinct and thus may respond differently to such interventions. However, some populations may be easier to control if they are isolated from external sources of parasites. Utu, having the least diverse and most genetically differentiated parasite population appears to be the most isolated catchment, and thus a location where malaria control strategies may be the most efficient.

A detailed understanding of the population genetics of P. falciparum can help guide malaria control efforts. Such knowledge is becoming paramount as the Papua New Guinean government prepares to intensify malaria control, not only for guiding these control efforts but also monitoring whether they are having an impact on parasite populations. This broad spatial survey of the population genetics of P. falciparum in Papua New Guinea has identified high but variable levels of genetic diversity, random associations among loci and population structure found at different spatial scales. The results have significant implications for malaria control in the Pacific region and show that countrywide population surveillance is needed throughout Papua New Guinea.