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Parasites & Vectors

, 12:2 | Cite as

Spatial distribution, prevalence and diversity of haemosporidians in the rufous-collared sparrow, Zonotrichia capensis

  • Daniela Doussang
  • Daniel González-Acuña
  • Luis Gonzalo Torres-Fuentes
  • Stephen C. Lougheed
  • Rute Beatriz Clemente-Carvalho
  • Kian Connelly Greene
  • Juliana A. ViannaEmail author
Open Access
Research

Abstract

Background

Parasite prevalence and diversity are determined by the distribution of hosts and vectors and by the interplay among a suite of environmental factors. Distributions of parasite lineages vary based on host susceptibility and geographical barriers. Hemoparasites of the genera Haemoproteus and Plasmodium have wide distributions, and high prevalence and genetic diversity within perching birds (Order Passeriformes). The rufous-collared sparrow (Zonotrichia capensis) is widely distributed in Central and South America across an immense diversity of environments from sea level to more than 4000 meters above sea level. It therefore provides an excellent model to investigate whether altitudinal and latitudinal gradients influence the distribution, prevalence and diversity of haemosporidian parasites, their population structure and the biogeographical boundaries of distinct parasite lineages.

Results

We assembled samples from 1317 rufous-collared sparrows spanning 75 locales from across Central and South America (between 9.5°N and 54°S; 10–4655 meters above sea level). We used DNA sequence data from a fragment of the mitochondrial cytochrome b gene (cytb) of Haemoproteus and Plasmodium from 325 positive samples and found prevalences of 22 and 3%, respectively. Haemoproteus exhibited a higher prevalence than Plasmodium but with comparatively lower genetic diversity. We detected a relationship of Plasmodium and Haemoproteus prevalence with altitude and latitude; however, altitude and latitude did not influence parasite diversity.

Conclusions

Parasite lineages showed a phylogeographical boundary coincident with the Andes Mountains, although we also observed a north-south disjunction in Peru for Haemoproteus. Haemosporidian distribution was not homogeneous but differed based on latitude and altitude. This is most probably due to environmental factors that have influenced both vector distribution and abundance, as well as parasite development. Our study provides key insights on the distribution of haemoparasite lineages and parasite dynamics within hosts.

Keywords

Avian malaria Avian host Plasmodium Haemoproteus Altitude Latitude 

Abbreviations

CI

Confidence interval

GD

Decimal degrees

GLMs

Generalized lineal models

H

Haplotype

Haem

Haemoproteus

Hd

Gene diversity

masl

Meters above sea level

Max

Maximum

Min

Minimum

N

Sample size

nH

Haplotype number

Plas

Plasmodium

S

Number of polymorphic sites

π

Nucleotide diversity

Background

In a rapidly changing world with many newly-emerging or geographically-expanding pathogens and parasites, we must investigate factors implicated in distribution of these organisms. Avian haemosporidia (Plasmodium, Haemoproteus, Leucocytozoon and Fallisia) are a group of blood parasites transmitted by vectors [1] and, due to their complex life-cycles, the prevalence, diversity, and distribution of these taxa are influenced by a dynamic interplay among hosts and their environment [2, 3]. Ecological factors such as the distribution, abundance and species richness of intermediate (birds) and definitive hosts (Diptera) regulate the transmission possibilities of hemoparasites [4, 5] and can promote their diversification. These ecological factors, in turn, may be influenced by the geography and evolutionary history of the hosts, providing opportunities to understand how host-parasite interactions influence parasite diversity [6, 7].

The distribution of avian haemosporidians differs among zoogeographical regions (Holarctic, Ethiopian, Oriental, Australian, Neotropical and Antarctic) [1]. The level of phylogeographical structure depends on the factors that most strongly influence parasite distributions and, in particular, we predict that such structure will be present if distributions are more related to factors like vector diversity and habitat heterogeneity [6]. Biogeographical patterns for distribution, prevalence and diversity of haemosporidian parasites have been described for multiple regions worldwide. Prevalences for both genera (Haemoproteus and Plasmodium) have been shown to be lower at higher altitudes [8, 9], with a greater limitation of Plasmodium at higher altitudes [10, 11].

Climate is closely linked to altitude and latitude, with lower temperatures occurring at higher altitudes and latitudes that could result in slower developmental rates of both parasite and vector [1]. Therefore, vector-borne diseases could impact hosts differently at different elevations, as rates of vector development and distribution could either limit or facilitate parasite transmission [10]. Thus, we expect that latitude may also relate to the presence of avian haemosporidians [12]. For example, the prevalence and diversity of these parasite lineages has been shown to increase at lower tropical latitudes [13, 14, 15]. In contrast, in a meta-analysis, Clark [16] found no correlation between parasite diversity and latitude worldwide; however, this study did not include considerations of avian host species in their analyses. This is a crucial factor since haemosporidia lineage diversity should relate to the density of susceptible avian hosts and to parasite-host specificity [17, 18]. Globally, Haemoproteus exhibits greater lineage diversity than Plasmodium; however, this pattern differs in South America, where a higher avian host diversity coupled with low Plasmodium-host specificity leads to greater lineage diversity of Plasmodium than Haemoproteus [15]. Haemoproteus lineages exhibit greater host specificity than Plasmodium lineages due to their high vector specialization on ceratopogonid and hippoboscid flies [1]. Several lineages of Plasmodium show extreme generalist host-parasitism strategies, while others appear to be restricted to particular host families over recent evolutionary history [4].

The rufous-collared sparrow is one of the most broadly-distributed passerines in the world, with a geographical range that spans the Americas from southern Mexico to Cape Horn (southern Chile) [19]. In the Southern Cone, they occur in an impressive diversity of environments, including coastal habitats, lowland desert, Patagonian steppe, scrub, grassland, Andean desert, forest, valley, and thorn scrub [20, 21]. This broad geographical range and habitat diversity makes this species an excellent subject for evaluating how habitat, latitude, altitude and evolutionary history might shape parasite prevalence and diversity. The evolutionary history of rufous-collared sparrows was influenced by major Pleistocene biogeographical events resulting in three main haplogroups: (i) spanning Central America, the Dominican Republic and north-western South America; (ii) encompassing the Dominican Republic, Roraima (Venezuela), La Paz (Bolivia) and south of Tierra del Fuego, Argentina; and (iii) eastern Argentina and Brazil [22]. Rufous-collared sparrows exhibit a great diversity of Haemoproteus and Plasmodium in Chile and other areas of South America [9, 13, 23, 24, 25, 26, 27, 28, 29, 30].

Previous studies of avian haemosporidians in wild birds have evaluated the phylogeny of the parasites, and tested for the possible effects of altitude [3, 10, 24] and latitude [16, 23] on haemosporidian diversity and prevalence. These studies, however, typically focused on small study areas and multiple avian host species, precluding evaluation of how environmental and evolutionary factors shape patterns within one avian host. In the present study, we investigate the distribution and prevalence of haemosporidians in a broad area of study and in a species-specific host. We hypothesized that haemosporidian distributions are shaped by both the evolutionary history of the avian host and the recognized biogeographical barriers in Central and South America. Furthermore, we hypothesized that haemosporidian distributions would show different prevalence and diversity across latitudinal and altitudinal environmental gradients. We predicted that parasite prevalence would vary with latitude for both genera and that Plasmodium would be restricted to lower altitudes relative to Haemoproteus. Differences in prevalence and genetic diversity of Haemoproteus and Plasmodium associated with altitude and latitude would also imply adaptation of these parasites to local environmental conditions.

Methods

Study area

We used a total 1317 samples of rufous-collared sparrow from 75 locations in Central and South America. Blood samples of 531 rufous-collared sparrows were collected during the period 2010–2016 from 29 localities across Chile, and these were combined with 59 other samples from 19 localities in Costa Rica, Bolivia, Peru and Argentina. An additional 727 samples from other locations that had already been assessed for haemosporidians were added from previous studies (Fig. 1, Additional file 1: Table S1 and Additional file 2: Table S2). Our 1317 samples thus span an extensive latitudinal (9.5°N to 54°S) and altitudinal (10–4655 meters above sea level, masl) range, which we used to quantify diversity and determine phylogeographical patterns and boundaries.
Fig. 1

Map of sampling localities and prevalence according to sampling area. Map of South America indicating sampled geographical locations (blue dots) and of other studies (yellow dots); numbers close to dots represent the number of the sample site (Additional file 1: Table S1). Distribution of rufous collared sparrows (Zonotrichia capensis) (light gray) is based on BirdLife International data. Pie charts exhibit prevalence of Haemoproteus (green) and Plasmodium (purple) and uninfected (gray) by sampling area

Sample collection

Adult birds were captured using mist nets in Chile. Blood samples were collected by puncturing the brachial vein [31] and 30–50 μl of blood was obtained and preserved in 1.5 ml microcentrifuge (Ependorff tube) tubes with 96% ethanol until subsequent processing in the laboratory. The rufous-collared sparrow samples from other countries (Costa Rica, Bolivia, Peru and Argentina) comprised 46 blood samples and 13 muscle, liver or heart tissues from specimens that were prepared as study skins for museum collections (Louisiana State Museum, USA).

DNA extraction, PCR amplification and sequencing

DNA was isolated using a salt extraction method developed by Aljanabi & Martínez [32]. DNA quality and concentration (ng/μl) were estimated using a NanoDrop 2000c spectrophotometer (Thermo Scientific, Waltham, Massachusetts, USA). We amplified a 533 bp fragment of the mitochondrial cytochrome b gene (cytb) of focal Haemoproteus/Plasmodium parasites using non-specific primers 3760F (5'-GAG TGG ATG GTG TTT TAG AT-3') and 4292Rw (5'-TGG AAC AAT ATG TAR AGG AGT-3') [33].

Polymerase chain reaction (PCR) reactions were carried out in final volumes of 30 μl, comprising 2 μl of template DNA, 1× reaction buffer, 1.5 mM MgCl2, 0.2 mM of each dNTP, 0.5 μM of each primer, and 1.25 units of Taq Platinum (Invitrogen, Carlsbad, California, USA). All PCR reaction sets included negative (ddH2O) and positive controls (samples previously confirmed by sequencing and microscopy). The PCR amplification profile was as follows: initial denaturation at 94 °C for 2 min; 40 cycles of denaturation at 95 °C for 40 s, annealing at 52 °C for 1 min and extension at 72 °C for 1 min; with a final extension at 72 °C for 10 min. PCR products were visualized using electrophoresis on 0.8% agarose gel with SB 1× buffer with GelRed™ [34]. Three different PCR reactions were conducted for each sample: one with isolated DNA template without controlling for concentration, and two other reactions with DNA concentrations of 50 and 20 ng/μl, respectively. Samples were considered positive when the parasite DNA was amplified in one of these three reaction conditions.

PCR products were purified and sequenced by Macrogen (Seoul, Korea). Sequences were edited and aligned using Sequencher v.5.4.5 (Gene Codes Corporation, Ann Arbor, Michigan, USA), and polymorphic sites were identified with ClustalX2.1 [35]. Haplotypes were identified using DNAsp v.5.10.1 software [36].

Prevalence, genetic diversity and population genetic structure

For prevalence estimates, five samples were excluded due to the lack of appropriate positive and negative controls in a previous study, leaving a total of 1312 samples. The prevalence of haemosporidian infection was calculated for all bird samples combined, as well as for each geographical region. Parasite prevalence for each sampling area was estimated as: P = number of infected hosts/number of sampled hosts × 100, using Excel software. The pooled prevalence for generalized linear model analyses was determined with 95% confidence intervals (CI) using the function binom.test (number of infected hosts, number sampled hosts, 0.5, alternative="two.sided", conf.level = 0.95) in R studio 386 3.0.1 [37].

Sampling locations were grouped according to country, with Chile being further subdivided into five natural geographical zones (Norte Grande, Norte Chico, Central, South and Austral) which corresponded to a north-south gradient of humidity varying from the Atacama Desert, through Mediterranean ecosystems, to temperate rainforest (see Table 1, Additional file 1: Table S1).
Table 1

Genetic diversity from cytochrome b sequences of Haemoproteus and Plasmodium by locality from 325 samples

Location

N

Haemoproteus

Plasmodium

N+

nH

S

Hd

π

N+

nH

S

Hd

π

Costa Rica

2

2

2

17

1

0.039

0

Colombia

428

19

3

9

0.578

0.003

3

3

2

1

0.003

Ecuador

1

1

1

0

1

0

0

Peru

211

56

3

11

0.284

0.006

9

4

36

0.694

0.039

Bolivia

6

1

1

0

1

0

1

1

0

1

0

Brazil

4

4

3

17

0.833

0.019

Uruguay

16

1

1

0

1

0

Argentina

47

13

2

10

0.282

0.006

6

4

46

0.866

0.049

Norte Grande Chile

140

49

4

33

0.157

0.006

2

2

1

1

0.002

Norte Chico Chile

187

48

1

0

0

0

1

1

0

1

0

Central Chile

207

94

2

17

0.082

0.003

11

3

33

0.618

0.039

South Chile

35

1

1

0

1

0

0

Austral Chile

33

2

2

12

1

0.027

1

1

0

1

0

Total

1317

286

10

50

0.325

0.008

39

18

68

0.931

0.050

Abbreviations: N, total number of samples; N+, number of positive samples; nH, number of haplotypes found; S, number of polymorphic sites; Hd, haplotype diversity; π, nucleotide diversity

Genetic diversity was measured for each geographical region using number of polymorphic sites (S), haplotype number (h), gene diversity (Hd), and nucleotide diversity (π) of cytb for both Haemoproteus and Plasmodium in Arlequin v.3.5 software [38]. Pairwise FST and ΦST were calculated between all location pairs to test for the signature of population differentiation. We also performed a Bayesian analysis of the population structure for cytb sequences using Bayesian Analysis of Population structure v.6 (BAPS) (http://www.helsinki.fi/bsg/software/BAPS/). This program partitions individuals into groups using maximum likelihood [39]. We used spatial cluster of group, ordering the lineages with the geographical coordinates of the localities where they were detected.

Biogeography and parasite distribution

We applied generalized linear models (GLMs) to identify possible effects of the latitude and altitude (explanatory variables) on the prevalence of infection and lineage genetic diversity such as haplotype and nucleotide (response variables). We evaluated each genus separately (Haemoproteus and Plasmodium) in R studio 386 3.0.1 [37] using GLM with a binomial error structure for prevalence and Poisson error for genetic diversity. All GLMs were subjected to residual analyses to evaluate the adequacy of the error distribution. For prevalence we included data from all locales with the exception of Costa Rica, Brazil, Bolivia and Ecuador because of the small sample sizes. Samples were grouped by country (according to geographical areas of sampling), and for Chile the aforementioned geographical areas were separated following a latitudinal gradient.

Phylogenetic analysis

The parasite sequences for our study were compared to other South America mtDNA cytb sequences using data available in MalAvi [14] and GenBank. The best nucleotide substitution model (GTR + I + G) was determined using JModeltest v.2.1.3 [40], applying both AIC (Akaike information criterion) and BIC (Bayesian information criterion) for Haemoproteus and Plasmodium separately.

To evaluate the relationship between the parasite haplotypes and clades with the geographical distribution and the Andes as a geographical boundary, we performed phylogenetic reconstruction in MrBayes v.3.1.2 [41]. We used 28 sequences (441 bp) in addition to Leucocytozoon toddi as an outgroup. The analysis was run for one million generations, sampling every 1000 generations to create a consensus tree; the standard deviation of the split criterion was less than 0.01. We considered nodes with posterior probabilities of 90% or more on the consensus tree to be robust support. The phylogeny was visualized using FigTree v.1.3.1 [42]. To further visualize the relationships among haplotypes and to evaluate genetic distinctiveness, we created a median-joining network using Network v.5.0 [43].

Results

Parasite prevalence, diversity and distribution

We found 325 rufous-collared sparrows that were positive for haemosporidian infection out of the total 1317, spanning 75 studied localities. This corresponded to 25% of all cases of Haemoproteus (n = 286) and Plasmodium (n = 39) detection. Prevalence differed markedly between genera.

Considering all of the data, the lowest prevalence was evident in Colombia for both Haemoproteus and Plasmodium, while in Peru, Argentina and Chile a higher prevalence was detected for Haemoproteus relative to Plasmodium. In Costa Rica and Bolivia, prevalence of Haemoproteus was high but sample sizes were small. In Costa Rica and Uruguay we found no Plasmodium. Haemoproteus showed low prevalence in Uruguay (6.3%) (Fig. 1). In Chile, we observed the highest prevalence of Haemoproteus in central (42%) and northern Chile (35%), with a low prevalence in the southern (2.8%) and austral (6%) areas (Additional file 1: Table S1). For Plasmodium, the highest prevalence occurred in Argentina (12.8%), central Chile (6.3%) and Peru (4.3%) (Fig. 1).

We identified a total of 28 parasite lineages based on 441 bp of cytb: 10 lineages of Haemoproteus and 18 lineages of Plasmodium. One Haemoproteus haplotype (haplotype 1) was the most frequent throughout the entire distribution; it was found in 233 of 325 positive samples (Fig. 2, Additional file 2: Table S2). This haplotype was found to be distributed from Peru, throughout all of Chile (except the austral location) and Argentina. All other Haemoproteus and Plasmodium lineages were found in only one or two rufous-collared sparrow individuals. The highest number of Haemoproteus haplotypes was found in Socoroma, in the north of Chile (18°S).
Fig. 2

Map of sampling localities and diversity for Haemoproteus (a) and Plasmodium (b). Map of South America indicating geographical locations with Haemoproteus positive samples (green dots) and Plasmodium positive samples (purple dots) (Additional file 1: Table S1); distribution of rufous collared sparrows (Zonotrichia capensis) (light gray) is based on BirdLife International data. Pie charts exhibit diversity of Haemoproteus (a) and Plasmodium (b)

Plasmodium showed a comparatively higher haplotype and nucleotide diversity (Hd = 0.931, π = 0.050) than Haemoproteus (Hd = 0.325, π = 0.008). Haemoproteus exhibited greater haplotype diversity at lower latitudes, decreasing toward southern Colombia (0.578), Peru (0.284), Argentina (0.282), Norte Grande, Chile (0.157), Norte Chico (0) and central Chile (0.082). The nucleotide diversity (π) for Haemoproteus varied between 0.003 and 0.0038 (Table 1). Plasmodium showed a greater haplotype diversity in Colombia (1), followed by Argentina (0.86), Brazil (0.83), Chile (0.74) and Peru (0.69), with nucleotide diversity (π) varying between 0.003 and 0.049 (Table 1).

The mean prevalence with confidence intervals grouped by country and geographical area used in our GLM analyses are shown in Additional file 3: Table S3. Results of our GLM analyses indicated that latitude and altitude had a significant effect on Haemoproteus (P < 0.001) and Plasmodium (P < 0.05) prevalence in South America (Table 2). The highest prevalence of Haemoproteus (Fig. 3a) and Plasmodium (Fig. 3c) was observed between 20 and 35°S (central Chile) and both genera decreased toward lower and higher latitude. Haemoproteus prevalence increased at higher altitudes up to approximately 2200 masl, where it began to decrease again (Fig. 3b) and Plasmodium prevalence increased at lower altitudes (Fig. 3d). Diversity was related neither to altitude nor latitude for either genus (Table 2).
Table 2

GLM analyses results Haemoproteus spp. and Plasmodium spp.

Response variable

GLM

Explanatory Variables

Coefficient

SE

z-value

P

Haemoproteus

 Total prevalence

Binomial

Altitude

-4.207e-04

6.564e-05

-6.409

1.46e-10***

Latitude

0.15835

0.02036

-7.777

7.43e-15***

 Total diversity

Poisson

Altitude

0.00006

0.00032

0.189

0.850

Latitude

0.00793

0.02164

0.367

0.714

Plasmodium

 Total prevalence

Binomial

Altitude

-0.0003592

0.0001750

-2.053

0.0401*

Latitude

-0.10548

0.05198

-2.029

0.0424*

 Total diversity

Poisson

Altitude

0.00029

0.00961

0.031

0.983

Latitude

-0.01224

0.56370

-0.022

0.975

*P < 0.05; ***P < 0.0001

Abbreviations: GLM, generalized linear model; SE, standard error

Fig. 3

Dispersion diagram for Haemoproteus and Plasmodium. Dispersion diagram of relationship between Haemoproteus prevalence with latitude and altitude (a and b), and relationship between Plasmodium prevalence with latitude and altitude (c and d) in South America

Phylogenetic analysis

The Bayesian phylogenies for cytb of Plasmodium and Haemoproteus of Chile and South America showed similar patterns to those evident in the median-joining networks (MJN). Our phylogenetic analysis provided strong support for four clades in Haemoproteus and for six clades in Plasmodium. For Haemoproteus, Clade II includes haplotype 1, the most common in our survey. Haemoproteus shows distinct phylogeographical patterns, with Clade I generally located at lower latitudes, and with haplotype 14 showing some restriction in distribution caused by the Andes. For Plasmodium, Clade I clearly encompasses countries that are on the east side of the Andes (Brazil, Bolivia, Argentina and Uruguay). These countries correspond to temperate latitudinal zones with some sampling locations in the tropical zone (Brazil) and have warm temperatures (Fig. 4 and Additional file 4: Figure S1) [44].
Fig. 4

Median-joining network for Haemoproteus and Plasmodium from cytochrome b mtDNA. Each circle in the network corresponds to a different haplotype, the size of the circles correspond to haplotype frequencies, the numbers associated to each circle correspond to the number of haplotypes, and the colors of the circles correspond to the different countries

Parasite phylogeographical pattern

Results of our BAPS analysis suggested three clusters (K = 3) for each genus (Haemoproteus and Plasmodium). For Haemoproteus the clusters corresponded to: (i) a region spanning Costa Rica to Norte Grande of Chile, including Bolivia, Argentina and Punta Arenas; (ii) an area encompassing the south of Peru, and locations in Chile such as south of Norte Grande, Chile, Norte Chico of Chile, and part of the Central area, Isla Mocha and Navarino islands; and (iii) a region that included central Chile (Termas del Flaco, Pantanillos and Parque Ingles). For Plasmodium, the clusters corresponded to: (i) Colombia, part of Peru and central Chile; (ii) part of Peru, Argentina and Chile; and (iii) Bolivia, Brazil, Uruguay and Argentina (Fig. 5).
Fig. 5

Bayesian analysis of population structure (BAPS). Type model population mixture analysis (spatial clustering of groups) shows 3 clusters (K = 3) for Haemoproteus and 3 clusters (K = 3) for Plasmodium spp.

Of 55 pairwise FST values between mtDNA Haemoproteus from different locations, 21 were significantly different from zero, as were 15 of 55 ΦST values (P < 0.05) (Additional file 5: Table S4 and Additional file 6: Tables S5). Most of these were comparisons between sites in north and central Chile, and other locations (Fig. 6). For Plasmodium, only 3 of 45 comparisons were significantly different from zero for FST, and 4 of 45 for ΦST (P < 0.05) (Additional file 7: Table S6 and Additional file 8: Table S7).
Fig. 6

Pairwise Fst and ΦST values for cytochrome b DNA sequences. Fst and Φst values for countries and geographical zones of Chile. *P < 0.05

Discussion

Patterns of prevalence and geographical distribution

The overall prevalence of Haemoproteus and Plasmodium in rufous-collared sparrows across Central and South America was 25%, varying among localities from 0 to 100%. Differences in prevalence among sampling places may be attributable to several factors involved in the transmission of hemoparasites, including identity and diversity of vector and host species, and abiotic environmental factors like precipitation, mean annual temperature and seasonality [2].

The high overall prevalence was underlain principally by the presence of the most common haplotype of Haemoproteus (H1). This haplotype had a higher prevalence at locations between 32–33°S, similar to the findings of Merino et al. [23], who reported the highest prevalence between 33–35°S (locales Rinconada and Pantanillos, respectively). The prevalence of Haemoproteus and Plasmodium were significantly affected by latitude, where the highest prevalence was observed in the central region of Chile west of the Andes (20–25°S) decreasing toward lower and higher latitudes. East of the Andes, northern Argentina also showed high prevalence for both parasites. A lower prevalence at more southerly latitudes (34–42°S) may be the result of lower annual temperatures that can result in lower developmental rates of both vector [45] and parasite [1]. Furthermore, for Haemoproteus and Plasmodium, the significant effect of latitude along the western Andes of South America is primarily attributable to low prevalence in Colombia. This low prevalence and high diversity of Haemoproteus in Colombia (see González et al. [9]) could be explained by the variability of habitats and hosts. Regions with high potential host diversity, such as Colombia, can reduce disease risk since pathogens are apportioned among many different hosts [46]. This may explain the potential dilution effect [47] for low parasite prevalence for rufous-collared sparrows found in Colombia.

Elevation has been suggested as a limiting factor for Plasmodium distribution due to lower temperatures at higher altitudes [10, 48, 49], resulting in a diminution of vectors with increasing elevation [10]. Imura et al. [3] attributed the low prevalence of Plasmodium and Haemoproteus among wild birds to the diminished abundance or even absence of appropriate vectors at high altitudes. In our study, we failed to detect Plasmodium above 600 m of altitude in Chile, Bolivia, Argentina, Brazil and Uruguay, consistent with this assertion. Plasmodium appears to be more sensitive to lower temperatures, with an optimal range of diurnal temperatures from 18 to 24 °C for development within vectors [50]. However, Haemoproteus prevalence increases with altitude, similar to patterns detected by Rooyen et al. [10], and declines above approximately 2000 m above sea level. Olsson-Pons et al. [51] suggested that infection patterns for hemoparasites are best predicted by geographical and abiotic factors for Plasmodium, but that host-parasite interactions are more important for predicting Haemoproteus.

Parasite diversity and distribution

Our study indicated a high genetic diversity for Plasmodium, but low genetic diversity for Haemoproteus in rufous-collared sparrows. Although diversity estimates were not statistically significantly related to altitude or latitude, a higher clade diversity (or lineages) was observed for both genera at lower latitudes.

This latitudinal diversity gradient may relate to temperature and precipitation, as these are abiotic variables that are known to enhance parasite diversification [52], but also to predict diversity of parasite hosts (birds and vectors). However, a recent study reported no influence of latitude or climate variation on the phylogenetic diversity of Haemoproteus and Plasmodium [16].

The proportionately higher diversity of Plasmodium compared to Haemoproteus (see also [15]), has been previously documented in rufous-collared sparrows [9, 24, 25, 26, 27, 28]. This difference in diversity may be caused by a lower specificity of Plasmodium for their host, but also because Plasmodium diversification is more likely influenced by host-switching [53]. Such host-switching would not produce a stable relationship over time [33], and thus would preclude the evolution of specialization. Thus, we can infer that the higher haplotype diversity in some sampled regions may relate to a greater number of potential avian host species.

Several lineages of Haemoproteus and Plasmodium that we found in rufous-collared sparrows have been reported by other authors [9, 23, 24, 25, 26, 27, 28]. Moreover, some of these parasite lineages have been found in other passerine species, which suggests some host-switching [17, 33] and a lack of host species specificity. Lineages of both parasite genera contain examples of specialization and generalism [33, 54]; however, multiple studies indicate that Haemoproteus is typically more host-specific than Plasmodium [4, 33, 55] and generally more constrained at the host family level [33]. For instance, Merino et al. [23] suggested that Haemoproteus is typically found within the passerine family Emberizidae, the family to which the rufous-collared sparrow belongs.

Haemoproteus haplotype H1 was the dominant haplotype in populations from Chile and Peru (see also [24]). Such a high prevalence and wide geographical distribution of a parasite implies parasite-host co-adaptation. This observation may also imply that haplotype H1 is endemic to those portions of South America. Endemic avian haemosporidian species tend to cause chronic disease with low virulence [1]. However the previously-noted difference between Haemoproteus and Plasmodium could be shifting as Haemoproteus shows signs of evolution from specialist to generalist tendencies in South America [56]; this might help explain the elevated genetic diversity of Haemoproteus that we found.

Parasite phylogeographical pattern

We found greater diversity in Plasmodium than Haemoproteus across surveyed regions, with a tendency to greater diversity at lower latitudes for both genera. For Haemoproteus we found a single dominant haplotype, but in both taxa we documented geographical patterns in the distribution of parasite lineages. For Haemoproteus we found a clear phylogeographical boundary in Peru. Interestingly, a similar phylogeographical boundary has been described for the avian host, with different rufous-collared sparrow haplogroups in Peru and Chile [22]. Aside from this boundary, the distributions of Haemoproteus and Plasmodium haplogroups in Central and South America do not seem to show patterns that are coincident with those present in the rufous-collared sparrow [22, 57]. Co-divergence histories of haemosporidian parasites with their avian hosts is dominated by host-switching events, and co-speciation is mostly observed at the family level rather than at the host population or species level [58].

One Haemoproteus haplotype (H15) was found in the austral region in Chile, and in northern countries (Peru, Ecuador, Colombia and Costa Rica), but was absent in the remaining sampled areas. This odd disjunction might be caused by avian migration, especially as the southernmost portion of Chile that corresponds to an overlap between two main migratory routes between the Northern and Southern Hemispheres [59]. Furthermore, bird migration has contributed to the wide distribution of haemosporidian parasites [1]. A major biogeographical boundary for avian species in South America is the Andes Mountains [60, 61]. Although results from our BAPs analysis (Fig. 5) grouped the samples from Argentina with the northern clade, there is a clear distinction in haplotype distribution between regions with a higher frequency of the haplotype H14. For Plasmodium, a distinct clade, consistent with results from BAPs that show Argentina, Uruguay, Brazil and Bolivia (Fig. 5) grouped together, supports the notion that the Andean massif limits gene flow in these parasites. Such assertions are preliminary and sampling of a greater geographical intensity is required for the eastern part of the Andes. Limited genealogical structure in Plasmodium across the remaining studied locations might be associated with a tendency towards host-parasite generalists with marked gene flow among different hosts, but this might also be a consequence of relatively low sample sizes, again meriting further study with larger arrays of samples.

Conclusions

The prevalence of Haemoproteus was markedly higher than Plasmodium, in contrast to patterns of haplotype diversity. This dichotomous observation may be attributable to the greater host specificity of Haemoproteus relative to Plasmodium. In South America, Haemoproteus and Plasmodium showed latitudinal and altitudinal patterns, with a prevalence peak between 20–40°S, followed by a decrease at higher latitudes. We found that Plasmodium prevalence increased at lower altitudes while Haemoproteus prevalence increased at higher altitudes. Our study is the first of Plasmodium and Haemoproteus for many of these regions in Latin America, and provides a map of hemoparasite prevalence and diversity within one of the most broadly-distributed passerine species in the world. Future studies should examine the prevalence of hemoparasites in other species of passerines, providing further information on parasite-host specificity. Our study adds to the current knowledge of prevalence and diversity of haemosporidian parasites. Low temperatures of the higher elevations can contribute to reduce the presence of avian hemosporidia and vectors. An increase in temperature due to climatic change could result in an increase in the latitudinal and altitudinal ranges of Haemoproteus and Plasmodium. This knowledge will also be useful in disease risk assessment for avian populations for their conservation.

Notes

Acknowledgements

The authors wish to thank SAG and CONAF for granting permits for bird catches. The authors would also like to thank Lucila Moreno, Fabián Beltrán, Pablo Olmedo, Nicolás Martin, Braulio Munóz, Karen Ardiles, Sebastián Muñoz-Leal, Walda Miranda, Iván Torres, María Carolina Silva, Consuelo Manosalva, Nicolás Fernández, Catalina Gutiérrez and María Ignacia Najle for their collaboration in sample collection.

Funding

This study was financed by Fondecyt 1130948 and 1170972 for the collection of samples and Conicyt for the analysis of samples.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its additional files. The newly generated sequences were deposited in the GenBank database under the accession numbers: MH444670-MH444688.

Authors’ contributions

DIDO, JAV and DAGA participated in the design of the study. DIDO, DAGA, LGTF, SCL and RBCC contributed to data collection. DIDO collected part of the data, performed molecular analyses, phylogenetic and statistical analyses, and drafted the manuscript. KG and RCC participated in sample analysis. DIDO, JVA and SCL participated in drafting the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study proposal was approved by Servicio Agrícola y Ganadero (SAG) (resolutions 8082; 1095; 3936; 2988), Corporación Nacional Forestal (CONAF) (resolutions 011; 002; 03; 019; XI-19-15) and the Bioethics Committee of the Universidad de Concepción (resolutions CE 03-2009; CER-18-2012), Chillán, Chile.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

13071_2018_3243_MOESM1_ESM.docx (45 kb)
Additional file 1: Table S1. Avian haemosporidian haplotypes prevalence with country, locality, latitude, longitude and altitude. (DOCX 45 kb)
13071_2018_3243_MOESM2_ESM.docx (49 kb)
Additional file 2: Table S2. Avian haemosporidian haplotypes used in phylogenetic reconstruction, with GenBank accession number and country. (DOCX 49 kb)
13071_2018_3243_MOESM3_ESM.docx (79 kb)
Additional file 3: Table S3. Prevalence and confidence intervals by country and geographical area grouped for the GLM analysis. (DOCX 78 kb)
13071_2018_3243_MOESM4_ESM.docx (224 kb)
Additional file 4: Figure S1. Bayesian phylogenetic reconstructions of Haemoproteus and Plasmodium species with available cyt b sequences (441 bp). Posterior probabilities of branch support are shown. Outgroup taxa correspond to Leucocytozoon toddy. (DOCX 224 kb)
13071_2018_3243_MOESM5_ESM.docx (45 kb)
Additional file 5: Table S4. Pairwise Fst values calculated from mtDNA Haemoproteus sequences between countries and geographical areas of Chile. (DOCX 45 kb)
13071_2018_3243_MOESM6_ESM.docx (45 kb)
Additional file 6: Table S5. Pairwise Φst values calculated from mtDNA Haemoproteus sequences between countries and geographical areas of Chile. (DOCX 44 kb)
13071_2018_3243_MOESM7_ESM.docx (44 kb)
Additional file 7: Table S6. Pairwise Fst values calculated from mtDNA Plasmodium sequences between countries and geographical areas of Chile. (DOCX 43 kb)
13071_2018_3243_MOESM8_ESM.docx (44 kb)
Additional file 8: Table S7. Pairwise Φst values calculated from mtDNA Plasmodium sequences between countries and geographical areas of Chile. (DOCX 43 kb)

References

  1. 1.
    Valkiunas G. Avian Malaria Parasites and other Haemosporidia. Boca Raton, FL: CRC Press; 2005.Google Scholar
  2. 2.
    Basto N, Rodríguez OA, Marinkelle CJ, Gutiérrez R, Matta N. Haematozoa in birds from La Macarena National Natural Park (Colombia). Caldasia. 2006;28:371–7.Google Scholar
  3. 3.
    Imura T, Suzuki Y, Ejiri H, Sato Y, Ishida K, Sumiyama D, et al. Prevalence of avian haematozoa in wild birds in a high-altitude forest in Japan. Vet Parasitol. 2012;183:244–8.CrossRefGoogle Scholar
  4. 4.
    Beadell JS, Covas R, Gebhard C, Ishtiaq F, Melo M, Schmidt BK, et al. Host associations and evolutionary relationships of avian blood parasites from west Africa. Int J Parasitol. 2009;39:257–66.CrossRefGoogle Scholar
  5. 5.
    Ivanova K, Zehtindjiev P, Mariaux J, Georgiev BB. Genetic diversity of avian haemosporidians in Malaysia: cytochrome b lineages of the genera Plasmodium and Haemoproteus (Haemosporida) from Selangor. Infect Genet Evol. 2015;31:33–9.Google Scholar
  6. 6.
    Isquiat F, Clegg SM, Phillimore AB, Black RA. OwensIPF, Sheldon BC. Biogeographical patterns of blood parasite lineage diversity and avian hosts from southern Melanesian islands. J Biogeogr. 2010;37:120–32.Google Scholar
  7. 7.
    Krasnov BR, Stanko M, Morand S. Host community structure and infestation by ixodid ticks: repeatability, dilution effect and ecological specialization. Oecologia. 2007;154:185–94.CrossRefGoogle Scholar
  8. 8.
    Rodríguez OA, Moya H, Matta NE. Avian blood parasites in the National Natural Park Chingaza: high Andes of Colombia. Hornero. 2009;24:1–6.Google Scholar
  9. 9.
    González AD, Lotta IA, García LF, Moncada LI, Matta NE. Avian haemosporidians from Neotropical highlands: evidence from morphological and molecular data. Parasitol Int. 2015;64:48–59.CrossRefGoogle Scholar
  10. 10.
    Rooyen JV, Lalubin F, Glaizot O, Christe P. Altitudinal variation in haemosporidian parasite distribution in great tit populations. Parasit Vectors. 2013;6:139.CrossRefGoogle Scholar
  11. 11.
    Harrigan RJ, Sedano R, Chasar AC, Chaves JA, Nguyen JT, Whitaker A, et al. New host and lineage diversity of avian haemosporidia in the northern Andes. Evol Appl. 2014;7:799–811.CrossRefGoogle Scholar
  12. 12.
    Quillfeldt P, Martínez J, Hennicke J, Ludynia K, Gladbach A, Masello JF, et al. Hemosporidian blood parasites in seabirds-a comparative genetic study of species from Antartic to tropical habitats. Naturwissenschaften. 2010;97:809–17.CrossRefGoogle Scholar
  13. 13.
    Durrant KL, Beadell JS, Ishtiaq F, Graves GR, Olson SL, Gering E, et al. Avian Hematozoa in South America: a comparison of temperate and tropical zones. Ornithol Monogr. 2006;60:98–111.CrossRefGoogle Scholar
  14. 14.
    Bensch S, Hellgren O, Pérez-Tris J. MalAvi: a public database of malaria parasites and related haemosporidians in avian hosts based on mitochondrial cytochrome b lineages. Mol Ecol Resour. 2009;9:1353–8.CrossRefGoogle Scholar
  15. 15.
    Clark NJ, Sonya MC, Lima MR. A review of global diversity in avian haemosporidians (Plasmodium and Haemoproteus: Haemosporida): new insights from molecular data. Int J Parasitol. 2014;44:329–38.CrossRefGoogle Scholar
  16. 16.
    Clark NJ. Phylogenetic uniqueness, not latitude, explains the diversity of avian blood parasite communities worldwide. Global Ecol Biogeogr. 2018;27:744–55.CrossRefGoogle Scholar
  17. 17.
    Bensch S, Stjernman M, Hasselquist D, Östman O, Hansson B, Westerdahl H, et al. Host specificity in avian blood parasites: a study of Plasmodium and Haemoproteus mitochondrial DNA amplified from birds. Proc Biol Sci. 2000;267:1583–9.CrossRefGoogle Scholar
  18. 18.
    Križanauskienė A, Hellgren O, Kosarev V, Sokolov L, Bensch S, Valkiunas G. Variation in host specificity between species of avian hemosporidian parasites: evidence from parasite morphology and cytochrome b gene sequences. J Parasitol. 2006;92:1319–24.Google Scholar
  19. 19.
    Chapman FM. The post-glacial history of Zonotrichia capensis. Bull Am Mus Nat His. 1940;77:381–438.Google Scholar
  20. 20.
    Couve E, Vidal CF, Ruiz J. Aves de Chile Sus islas oeánicas y península Antártica. Una guía de campo ilustrada. Chile: FS Editorial: Punta Arenas; 2016. p. 451.Google Scholar
  21. 21.
    Ortiz D, Capllonch P. La migración del chingolo (Zonotrichia capensis) en Argentina. Hist Nat. 2011;1:105–9.Google Scholar
  22. 22.
    Lougheed SC, Campagna L, Dávila JA, Tubaro PL, Lijtmaer DA, Handford P. Continental phylogeography of an ecologically and morphologically diverse Neotropical songbird, Zonotrichia capensis. BMC Evol Biol. 2013;13:58.CrossRefGoogle Scholar
  23. 23.
    Merino S, Moreno J, Vásquez RA, Martínez J, Sánchez-Monsálvez I, Estades CF, et al. Haematozoa in forest birds from southern Chile: latitudinal gradients in prevalence and parasite lineage richness. Austral Ecol. 2008;33:329–40.CrossRefGoogle Scholar
  24. 24.
    Jones MR, Cheviron ZA, Carling MD. Spatial patterns of avian malaria prevalence in Zonotrichia capensis on the western slope of the Peruvian Andes. J Parasitol. 2013;99:903–5.CrossRefGoogle Scholar
  25. 25.
    Lacorte GA, Félix GMF, Pincheiro RRB, Chaves AV, Almeida-Neto G, Neves FS, et al. Exploring the diversity and distribution of neotropical avian malaria parasites - a molecular survey from southeast Brazil. PLoS One. 2013;8:e57770.Google Scholar
  26. 26.
    Marzal A, García-Longoria L, Cárdenas Callirgos JM, Sehgal RN. Invasive avian malaria as an emerging parasitic disease in native bird of Peru. Biol Invasions. 2014;17:39–45.CrossRefGoogle Scholar
  27. 27.
    Walther EL, Valkiunas G, González AD, Matta NE, Ricklefs RF, Cornel A, et al. Description, molecular characterization, and patterns of distribution of a widespread new world avian malaria parasite (Haemosporida: Plasmodiidae), Plasmodium (Novyella) homopolare sp. nov. Parasitol Res. 2014;113:3319–32.CrossRefGoogle Scholar
  28. 28.
    Mantilla JS, González AD, Lotta IA, Moens M, Pacheco MA, Escalante AA, et al. Haemoproteus erythrogravidus n. sp. (Haemosporida, Haemoproteidae): description and molecular characterization of a widespread blood parasite of birds in South America. Acta Trop. 2016;159:83–94.Google Scholar
  29. 29.
    Soares L, Escudero G, Penha VAS, Ricklefs RE. Low prevalence of haemosporidian parasites in shorebirds. Ardea. 2016;104:129–41.CrossRefGoogle Scholar
  30. 30.
    Fecchio A, Pinheiro R, Felix G, Faria IP, Pinho JB, Lacorte GA, et al. Host community similarity and geography shape the diversity and distribution of haemosporidian parasites in Amazonian birds. Ecography. 2017;41:505–15.CrossRefGoogle Scholar
  31. 31.
    Campbell TW, Coles EH. Avian clinical pathology. In: Coles EH, editor. Veterinary Clinical Pathology. 4th ed. Philadelphia, USA: W. B. Saunders Company; 1986. p. 279–301.Google Scholar
  32. 32.
    Aljanabi SM, Martinez I. Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques. Nucleic Acids Res. 1997;25:4692–3.CrossRefGoogle Scholar
  33. 33.
    Beadell JS, Gerin E, Austin J, Dumbacher JP, Peirce MA, Pratt TK, et al. Prevalence and differential host-specificity of two avian blood parasite genera in Australo-Papuan region. Mol Ecol. 2004;13:3829–44.CrossRefGoogle Scholar
  34. 34.
    Brody JR, Kern SE. Sodium boric acid: a Tris-free, cooler conductive medium for DNA electrophoresis. Biotechniques. 2004;36:214–6.Google Scholar
  35. 35.
    Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan P, McWilliam H, et al. Clustal W and Clustal X version 2.0. Bioinformatics. 2007;23:2947–8.CrossRefGoogle Scholar
  36. 36.
    Rozas J. DNA sequence polymorphism analysis using DNASP. In: Posada D, editor. Bioinformatics for DNA sequence analysis. Methods in Molecular Biology Series Vol. 537. New Jersey: Humana Press; 2009. p. 337–50.Google Scholar
  37. 37.
    R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.Google Scholar
  38. 38.
    Excoffier L, Lischer HEL. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010;10:564–7.CrossRefGoogle Scholar
  39. 39.
    Corander J, Sirén J, Arjas E. Bayesian spatial modeling of genetic population structure. Contr Stat. 2008;23:111–29.Google Scholar
  40. 40.
    Posada D. jModelTest: phylogenetic model averaging. Mol Biol Evol. 2008;25:1253–6.CrossRefGoogle Scholar
  41. 41.
    Huelsenbeck JP, Ronquist F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics. 2001;17:754–5.CrossRefGoogle Scholar
  42. 42.
    Rambaut A. FigTree v1.4.0: Tree Figure Drawing Tool. 2009. http://tree.bio.ed.ac.uk/software/figtree/.
  43. 43.
    Bandelt H, Forster P, Röhl A. Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol. 1999;16:37–48.CrossRefGoogle Scholar
  44. 44.
    Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World map of the Köppen-Geiger climate classification updated. Meteorol Z. 2006;15:259–63.CrossRefGoogle Scholar
  45. 45.
    Zamora-Vilchis I, Williams SE, Johnson CN. Environmental temperature affects prevalence of blood parasites of birds on an elevation gradient: implications for disease in a warming climate. PLoS One. 2012;7:e39208.CrossRefGoogle Scholar
  46. 46.
    Avendaño JE, Bohórquez CI, Roselli L, Arzuza-Buelvas D, Estela FA, Cuervo AM, et al. Lista de chequeo de las aves de Colombia: una síntesis del estado del conocimiento desde Hilty & Brown (1986). Ornitol Colomb. 2017;16:eA01.Google Scholar
  47. 47.
    Keesing F, Holt RD, Ostfeld RS. Effects of species diversity on disease risk. Ecol Lett. 2006;9:485–98.CrossRefGoogle Scholar
  48. 48.
    Atkinson CT, Dusek RJ, Woods KL, Iko WM. Pathogenicity of avian malaria in experimentally-infected Hawaii amakihi. J Wildlife Dis. 2000;36:197–204.CrossRefGoogle Scholar
  49. 49.
    Atkinson CT, Saili KS, Utzurrum RB, Jarvi SI. Experimental evidence for evolved tolerance to avian malaria in a wild population of low elevation Hawai’i’ amakihi (Hemignathus virens). Ecohealth. 2013;10:366–75.Google Scholar
  50. 50.
    Loiseau C, Harrigan RJ, Bichet C, Julliard R, Gamier S, Lendvai AZ, et al. Predictions of avian Plasmodium expansion under climate change. Sci Rep. 2013;3:1126.CrossRefGoogle Scholar
  51. 51.
    Olsson-Pons S, Clark NJ, Ishtiaq F, Clegg SM. Differences in host species relationships and biogeographic influences produce contrasting patterns of prevalence, community composition and genetic structure in two genera of avian malaria parasites in southern Melanesia. J Anim Ecol. 2015;84:985–98.Google Scholar
  52. 52.
    Santiago-Alarcón D, Palinauskas V, Schaefer HM. Diptera vectors of avian haemosporidian parasites: untangling parasite life cycles and their taxonomy. Biol Rev. 2012;874:928–64.CrossRefGoogle Scholar
  53. 53.
    Lauron EJ, Loiseau C, Bowie RCK, Spicer GS, Smith TB, Melo M, et al. Coevolutionary patterns and diversification of avian malaria parasites in Africa sunbirds (family Nectariniidae). Parasitology. 2014;142:635–47.Google Scholar
  54. 54.
    Hellgren O, Pérez-Tris J, Bensch S. A jack-of-all-trades and still a master of some: prevalence and host range in avian malaria and related blood parasites. Ecology. 2009;90:2840–9.Google Scholar
  55. 55.
    Atkinson CT. Vectors, epizootiology, and pathogenicity of avian species of Haemoproteus (Haemosporina: Haemoproteidae). Bull Soc Vector Ecol. 1991;16:109–26.Google Scholar
  56. 56.
    Moens MAJ, Pérez-Tris J. Discovering potential sources of emerging pathogens: South America is a reservoir of generalist avian blood parasites. Int J Parasitol. 2015;46:41–9.CrossRefGoogle Scholar
  57. 57.
    Campagna L, Kopuchian C, Tubaro PL, Lougheed SC. Secondary contact followed by gene flow between divergent mitochondrial lineages of a widespread Neotropical songbird (Zonotrichia capensis). Biol J Linn Soc. 2014;111:863–8.CrossRefGoogle Scholar
  58. 58.
    Santiago-Alarcón D, Rodríguez-Ferraro A, Parker PG, Ricklefs RF. Different meal, same flavor: cospeciation and host switching of haemosporidian parasites in some non-passerine birds. Parasit Vectors. 2014;7:286.CrossRefGoogle Scholar
  59. 59.
    Kirby JS, Stattersfield AJ, Butchart SHM, Evans MI, Grimmett RFA, Jones VR, et al. Key conservation issues for migratory land- and waterbird species on the world’s major flyways. Bird Conserv Int. 2008;18:S49–73.CrossRefGoogle Scholar
  60. 60.
    Antonelli A, Nylander JAA, Persson C, Sanmartin I. Tracing the impact of the Andean uplift on Neotropical plant evolution. Proc Natl Acad Sci USA. 2009;106:9749–54.CrossRefGoogle Scholar
  61. 61.
    Zemlak TS, Habit EM, Walde SJ, Battini MA, Adams EDM, Ruzzante DE. Across the southern Andes on fin: glacial refugia, drainage reversals and a secondary contact zone revealed by the phylogeographical signal of Galaxias platei in Patagonia. Mol Ecol. 2008;17:5049–61.CrossRefGoogle Scholar

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© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Daniela Doussang
    • 1
    • 2
  • Daniel González-Acuña
    • 1
  • Luis Gonzalo Torres-Fuentes
    • 1
  • Stephen C. Lougheed
    • 3
  • Rute Beatriz Clemente-Carvalho
    • 3
  • Kian Connelly Greene
    • 2
  • Juliana A. Vianna
    • 2
    Email author
  1. 1.Facultad de Ciencias VeterinariasUniversidad de ConcepciónChillánChile
  2. 2.Departamento de Ecosistemas y Medio Ambiente, Facultad de Agronomía e Ingeniería ForestalPontificia Universidad Católica de ChileSantiagoChile
  3. 3.Department of BiologyQueen’s UniversityKingstonCanada

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