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Demographic and Historical Processes Influencing Cochliomyia
hominivorax (Diptera: Calliphoridae) Population Structure across South
America
Kelly da Silva e Souza1, Letícia Chiara Baldassio de Paula1, Ana Maria Lima de
Azeredo-Espin2†,Tatiana Teixeira Torres1*
1Department of Genetics and Evolutionary Biology. Institute of Biosciences. University of
São Paulo, Brazil
2Molecular Biology and Genetic Engineering Center, Campinas State University, Brazil
†Deceased 25 May 2022
*Correspondence: Depto. Genética e Biologia Evolutiva, Instituto de Biociências,
Universidade de São Paulo, Rua do Matão, 277, 05508-090, São Paulo, SP, Brasil. +
55-11-3091-8759. E-mail: tttorres@ib.usp.br
Abstract
Background: This study investigates the genetic variability and population structure of
Cochliomyia hominivorax, the New World screwworm fly. This study tested the hypothesis
that the species exhibits a center-periphery distribution of genetic variability, with higher
genetic diversity in central populations (e.g., Brazil) and lower diversity in peripheral
populations. Methods: Utilizing microsatellite markers, we analyzed larvae collected from
infested livestock across South America. Larvae were collected directly from various wound
sites to ensure a broad representation of genetic diversity. Results: Contrary to our initial
hypothesis, the results revealed consistent genetic variability across the species' distribution,
low population differentiation, and no evidence of isolation-by-distance patterns among
subpopulations. The genetic analysis indicated an excess of homozygotes, potentially due to
the Wahlund effect, null alleles, or selection pressure. Conclusions: These findings suggest a
complex metapopulation structure for Co. hominivorax, challenging classical population
genetics models. This complexity likely arises from the species' high dispersal capability and
frequent local extinctions followed by recolonization. These results have important
implications for the design and implementation of control programs, emphasizing the need
for coordinated and large-scale actions rather than isolated initiatives.
Keywords: New World screwworm; myiasis; genetic variability; metapopulation; isolation
by distance; center-periphery distribution.
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Background
Parasitic diseases significantly influence livestock production, especially in underdeveloped
and developing nations [1,2]. These diseases can lead to a range of detrimental effects on
livestock, including increased mortality rates, decreased fertility, weight loss, and diminished
milk production, thereby causing a significant impact on production levels, both directly and
indirectly [3]. Ecto- and endoparasites are the main culprits, recognized for restricting animal
production by compromising the health of cattle, sheep, goats, and other livestock species.
Given that over a billion people worldwide rely on livestock, managing and controlling
parasitic infections has become a high priority to maintain food security and reduce economic
losses [4].
Myiasis, an infection caused by Diptera larvae feeding on living or dead tissues of a
vertebrate host, liquid body material, or ingested food [5], is a significant concern for
livestock production. Several fly species have been identified as responsible for some form of
myiasis, with the most important species belonging to the families Muscidae, Calliphoridae,
Oestridae, and Sarcophagidae [6]. These species hold medical, sanitary and economic
importance, affecting humans and other animals, while contributing to the spread of
pathogenic and agricultural losses [5,7].
The New World screwworm (NWS) fly, Cochliomyia hominivorax [8] is an obligate
ectoparasite of homeothermic animals [5]. Adult females of this species deposit their eggs in
wounds or cavities on the host’s body, with the larvae feeding on living tissue. If not treated,
wounds can expand, attracting other females to lay their eggs in the same host [9,10]. This
species is a major pest in the Neotropical region, infesting domestic animals, mostly cattle
[11]. While infestation by screwworm larvae can lead to the host’s death in extreme cases,
common manifestations include abortion, reduction in milk production, weight, and fertility
[12]. Additionally, infestation scars reduce leather quality, affecting its value [13]. Economic
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losses caused by this species are substantial, encompassing not only reduced productivity and
death, but also the costs of manpower and insecticides involved in the management,
prophylaxis, and treatment. Estimates indicate that annual costs in South America can reach
3.6 billion dollars annually [14].
Originally, the geographical distribution of the screwworm fly extended from the
southern United States to the northern regions of Argentina and Uruguay [7]. Significant
economic losses throughout its distribution led to initiatives to control the fly through male
sterilization and the implementation of an eradication program, the Sterile Insect Technique
(SIT) [15]. The Co. hominivorax eradication program by SIT began in 1958 in the United
States, leading to the country being declared free of cases by 1966. Similar successes were
achieved throughout Central America to Panama in 2001, with a permanent barrier
maintained since then to prevent recolonization by flies from South America [16–19].
Despite these efforts, outbreaks such as the one in Florida Keys in 2016 have occurred but
were successfully re-eradicated within six months [20].
Due to the high cost of SIT, the control of Co. hominivorax in its current geographical
distribution relies on insecticides, mostly organophosphates. However, their extensive use
poses serious risks, including toxicity to animals and meat consumers, environmental
pollution, and the selection of resistant strains [21–23]. Cochliomyia hominivorax is a
significant agricultural pest with wide dispersal and adaptability, necessitating continuous
investment in monitoring, treatment, and control. Therefore, research in genetics and ecology
is crucial for developing effective control strategies. The status of Co. hominivorax as a
model species for a well-established control program underscores its significance, motivating
the study of its biology. Among these, genetic variability and analysis of natural population
structure are essential. By investigating these patterns, valuable insights into NWS
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management can be gained, leading to the development of targeted interventions to mitigate
its impact on agricultural systems.
Population genetics of Co. hominivorax
The first genetic studies for Co. hominivorax aimed at characterizing mutations caused by the
exposure of pupae to radiation [24]. Almost 20 years later, genetic studies were resumed
using isozymes to characterize the variation generated by pupae irradiation. Subsequently,
Bush and Neck [25] identified variations in two loci of enzymes related to the production of
energy for flight, aGPDH, and PGM, when analyzing samples from the mass rearing of Co.
hominivorax.
McInnis [26], Richardson et al. [27,28], and Azeredo-Espin [29] initiated studies in
North America and Brazil, aiming at the characterization of natural populations with
karyotypic, morphological, and sexual compatibility analyses. These studies found a great
intra and interpopulation variability in the Co. hominivorax populations. Dev et al. [30] used
cytological analysis of polytene chromosomes from samples of Co. hominivorax from 10
different localities (nine in Mexico and one in Jamaica) but did not identify reproductively
isolated populations. In the late 1980s, molecular markers began to be used to describe the
genetic variability and structure of natural populations of Co. hominivorax.
Initially, mitochondrial DNA (mtDNA) restriction profiles were used to analyze
geographic populations of Co. hominivorax from the United States, Mexico, Costa Rica,
Guatemala, and Jamaica [31,32]. The authors found a high variability, significant population
structure, and reduced gene flow, mainly between continental samples and samples from the
island of Jamaica.
Krafsur and Whitten [33], using three polymorphic isozyme loci, found no population
differentiation in 11 geographic samples from Mexico. They concluded that Co. hominivorax
constitutes a single panmictic population. A high level of gene flow was also found for Co.
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hominivorax from different locations in Costa Rica, indicating the absence of genetic
structuring [34]. Comparing previously analyzed samples from Mexico and Costa Rica with
samples collected in Rio de Janeiro and Rio Grande do Sul, the authors obtained concordant
results [35]. The results indicated there was no evidence of population structuring, and the
hypothesis formulated was that there was a high rate of gene flow between populations.
On the other hand, mtDNA analysis of geographic populations of Co. hominivorax
from Brazil revealed a high intra and interpopulation mitochondrial genotypes variability
[36,37]. Studies with RFLP, RAPD, and isozymes indicated that the analyzed samples of Co.
hominivorax present high levels of variability and genetic differentiation, indicating
population structuring and reduced gene flow [37–39].
In a study carried out with samples from seven locations in Uruguay [40], no
population differentiation was detected. This fact is attributed to the absence of geographical
barriers in Uruguay and the passive transport of larvae by host transport. In Uruguay, Torres
et al. [41] utilized mtDNA and microsatellite markers and did not identify significant
population structuring, observing greater variation within the seven populations than between
them, and all populations with remarkably similar allele distributions. Lyra et al. [42]
identified a moderate structuring of Co. hominivorax populations in a study across 34
locations spanning 10 continental and island countries in Central and South America. In this
study, analysis of mitochondrial DNA (mtDNA) data using PCR-RFLP technique unveiled a
complex pattern of population genetic structure, with an analysis focusing solely on the
islands revealed significant population structure (ФST = 0.5234; P < 0.001) and low
population variability, indicating that the islands function as independent evolutionary units
connected by limited gene flow. By contrast, high variability and low, but significant,
differentiation was found among mainland populations (ФST = 0.0483; P < 0.001), which
could not be attributed solely to geographic distance. Torres and Azeredo-Espin [43] utilized
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12 microsatellites to investigate the population genetics of Co. hominivorax in the Caribbean.
They detected moderate population structure among populations (FST = 0.157) and high
population differentiation, suggesting highly structured populations resulting from either
limited gene flow or a source-sink dynamic, along with rapid recovery from population
contractions.
In an exploration of the phylogeographic history of Co. hominivorax, Fresia et al. [44]
delved into mitochondrial DNA sequences from 361 individuals sampled across 38 locations
within its contemporary range. Their findings revealed significant genetic divergence on a
macrogeographic scale (ΦST = 0.496; P < 0.001), suggesting historical events as primary
drivers shaping genetic diversity distribution, such as Caribbean Island colonization, Amazon
region vicariance, and population expansion. In a more recent study, Tietjen et al. [45] found
a spectrum of population genetic differentiation from “moderate” to “very large” levels
employing SNPs to elucidate the population structure of Co. hominivorax at 12 sites spanning
its entire distribution area.
These previous studies of natural populations of Co. hominivorax have yielded
different results, which may appear contradictory. However, a discernible pattern for the
distribution of genetic variability in this species can be inferred. In general, these results have
indicated a low to moderate genetic structuring in the regions situated at the distribution
extremes of this species, contrasting with a high structuring observed in central regions. This
pattern suggests a center-periphery distribution of genetic variability. The central populations
in Brazil, considered the origin center of the species [39], would harbor older populations and
display a pattern of historical differentiation. Populations situated at the distribution
peripheries would result from recent colonization events and hence exhibit low levels of
population structuring. In this scenario, there would be a population structure following the
isolation-by-distance model, with peripheral populations presenting a lower genetic
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variability than those at the center of the distribution. In light of these observations, our study
aimed to investigate the genetic variability and assess the degree of genetic structuring among
Co. hominivorax geographical populations from South America using microsatellite markers,
testing the hypothesis of center-periphery distribution of genetic variability.
Material and Methods
Obtaining Co. hominivorax samples from South America
Cochliomyia hominivorax larvae were collected directly from wounds of infested animals on
livestock farms at eight locations in Brazil. Samples from the remaining locations were
obtained from wounds of infested animals and shipped in absolute ethanol by local farmers
and collaborators (Table 1, Fig. 1). The samples were collected between 2001 and 2006.
These samples were the same samples Lyra and colleagues analyzed in 2009, and Fresia and
colleagues in 2011, both with mitochondrial markers [42,44].
Analysis of microsatellite data
Detection of microsatellite loci polymorphism
To analyze the geographic populations of South America, we used twelve loci selected from
previous work [46,47]. PCR amplification of microsatellite loci detailed in “Additional file 1:
Table S1.” and visualization were performed as previously described by Torres et al. ([46];
Table S1).
The number and frequency of alleles, as well as the observed (HO) and unbiased
expected (HE) [48] heterozygosities under Hardy-Weinberg equilibrium, were determined per
locus for each location. The ‘basic.stats’ function of the R package hierfstat version 0.5.10
[49] was applied to calculate average observed heterozygosity (HO), expected heterozygosity
(HE), and inbreeding coefficients (FIS). Deviations from Hardy-Weinberg equilibrium
expectations were assessed for each locus and population using exact tests implemented in
GENEPOP 4.7 [50,51].
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Allele frequency data were utilized, and statistical tests applied to detect signatures of
heterozygosity excess (HExc), indicative of a recent bottleneck event, using the program
BOTTLENECK 1.2.02 for all microsatellite loci [52]. The sign test was employed to evaluate
the number of loci exhibiting heterozygosity excess compared to the expected number by
chance under different mutational models, including the infinite alleles model (IAM), the
stepwise mutation model (SMM), and the two-phase model (TPM). For the TPM we chose
the proportions in favor of IAM (30% of SMM and 70% of IAM).
Interpopulation differentiation was measured in terms of FST estimates in samples
from South America. Three sets of subpopulations were used to calculate interpopulation
differentiation in terms of FST estimation, inbreeding coefficient FIS and estimation number of
migrants Nm. The first set includes all samples from Table 1, excluding only samples
obtained from Uruguay in 2004. The second set excludes only samples obtained from
Uruguay in 2003. The third group includes all samples from Table 1, comprising both
samples from Uruguay in 2004 and 2003 without grouping them. The genetic differentiation
index, FST, was calculated between localities using absolute allele frequencies, following the
methodology described by Weir and Cockerham [53], implemented via the hierfstat library in
R [49]. Pairwise FST estimates were assessed for significance through genotype permutation
among populations, as outlined by Goudet et al. [54]. The critical significance level applied
in all statistical tests was 0.05. In all simultaneous statistical tests, critical significance levels
were corrected using the Sequential Bonferroni test [55] to enable the overall significance to
be examined. The isolation-by-distance model, assessing population genetic structure, was
evaluated through linear regression. This involved correlating pairwise FST /(1 - FST) with the
natural logarithm of the geographical distance between population pairs [56]. Additionally,
comparisons were made between the sample groups' genetic variability using “Wilcoxon
signed rank” tests. These analyses were performed on the R [57] computational platform.
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Results
Genetic variability
The number of alleles observed heterozygosity (HO), and expected heterozygosity (HE) were
calculated per locus and population (Additional file 2: Table S2; Additional file 3 Fig. S1).
The number of alleles detected per locus ranged from 2 to 18, with an average of 6.9 alleles
per locus and per population. The expected heterozygosity ranged from 0.194 (Costa Rica,
Brazil, locus CH09) to 0.936 (Costa Rica, Brazil, locus CH21), with an overall average of
0.722. With the exclusion of samples from Uruguay, the average did not change, and it was
0.727. Significant deviation from the Hardy-Weinberg equilibrium were found in 187 out of
348 tests, though after sequential Bonferroni correction, this number reduced to 124
significant tests. Such deviations were consistent across all analyzed samples, manifesting in
at least one locus per location. Linkage disequilibrium analysis revealed that 376
comparisons between loci pairs out of 1782 showed linkage disequilibrium (p < 0.05).
Additionally, this analysis revealed non-random associations between loci pairs in 56
comparisons After sequential Bonferroni correction. These imbalances varied across
subpopulations.
The analysis of all the loci revealed the least variability in expected heterozygosity in
Colonia del Sacramento, Uruguay (2004), with HEequal to 0.633 (Fig. 1), significantly
different from the overall mean (Wilcoxon Signed-rank test, p = 0.001). Similarly, Joaquín
Suarez, Uruguay (2003), exhibited lower mean variability (HE= 0.622; Wilcoxon
Signed-rank test, p = 0.0243). Conversely, the highest observed variabilities were recorded in
Joaquín Suarez, Uruguay (2004) (0.774; p = 0.02), Santa Maria das Barreiras, Brazil (0.775;
p = 0.0018), and Caiapônia, Brazil (0.778; p = 0.0001). Heterozygosity was also significantly
different from the overall mean in the populations of San Antonio, Uruguay (2003 and 2004),
Cerro Colorado, Uruguay (2004), and Campinas, Brazil, as shown in “Additional file 2: Table
S2”. Other observed differences from the mean were not statistically significant.
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To test the decrease in the genetic variability in the extreme south of the Co.
hominivorax distribution, a test was carried out comparing the HEof the southern locations
(below the Capricorn Tropic), including all the subpopulations of Uruguay and the four of
Southern Brazil with populations of the central region (the remaining Brazil and Venezuela
subpopulations). The Co. hominivorax variability in the south (HE= 0.7206) was not
significantly different from the variability of central populations (HE= 0.7372), with p =
0.251. Additionally, no significant difference was observed when comparing allelic
diversities (“allelic richness”, DA) between southern and central groups (DAsouth = 4.71;
DAcentral = 4.94; p = 0.10987) or when considering only samples from Uruguay as a southern
group (HE Uruguay = 0.7206, HE central = 0.7372 and p = 0.5658; DAUruguay = 4.84, DAcentral = 4.94
and p = 0.16967).
A bottleneck was not detected in any of the subpopulations across all three mutation
models simultaneously (Table 2). However, in most of them, except for San Antonio,
Uruguay (2003), Santo Antonio das Missões, and São Sebastião do Paraíso, Brazil, an excess
of heterozygotes was detected in relation to the number of heterozygotes expected at
equilibrium in the IAM model. Under the SMM model, nine populations putatively
experienced a reduction in population size (San Antonio and Banãdos de Medina 2003,
Pinheiro Machado, Santo Antonio das Missões, Carambeí, Estiva, São Sebastião do Paraíso,
Caiapônia, and Goiânia). Under TPM, there was a significant population reduction in San
Antonio (2004) and Cerro Colorado (2004).
Genetic differentiation
Genetic differentiation index (FST) were significant and small across subpopulation groups
(Table 3), with moderate inbreeding coefficient (Table 3) (FIS), and moderate estimated
number of migrants (Table 3). The global FST estimates were low but significant, indicating a
population structure for the analyzed samples. Even in a species with a high dispersal rate,
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these values are surprisingly low given the vast territorial extension covered (samples from
up to 5000 km away were compared).
The FST pairwise estimates were also generally low and different from zero for 264
out of 276 pairs within group 1 (Fig. 2a), and for 220 out of 231 pairs within group 2 (Fig.
2b). For group 3, the estimates were also different from 0 for 371 out of 406 pairs, ranging
from 0.00686 (between Colonia, Uruguay 2003 and Banãdos de Medina, Uruguay 2003) to
0.13815 (between Joaquín Suarez, Uruguay 2003 and Colonia, Uruguay 2004). Nine
subpopulations that presented all significant estimates in group 3 (Joaquín Suarez, Uruguay
2004; Colonia , Uruguay 2004; San Antonio Uruguay 2003, 2004; Banãdos de Medina 2004;
Cerro Colorado 2003, 2004; Campinas, Brazil; Barquisimeto and Encontrados, Venezuela)
differed from all other populations. Nevertheless, estimates were low, ranging from 0.0251
(with São Sebastião do Paraíso) and 0.1193 (with Colonia del Sacramento (2004), Uruguay)
(Fig. 2c).
The correlation between the genetic and geographic distances was not significant after
the Mantel test neither in the group 1 samples (p = 0.1954, Fig. 3a) nor in the group 2
samples (p = 0.137. Fig. 3b). This pattern did not change even when excluding locations
separated by distances inferior to 100 km (group 1, p = 0.116; group 2, p = 0.158) or 200 km
(group 1, p = 0.134; group 2, p = 0.152). Tests using other minimum and maximum distances
also failed to indicate any genetic and geographic distance association (data not shown).
The low differentiation found between subpopulations was not expected at such large
distances (for example, FST = 0.0534 estimation between samples presenting the greatest
geographic distance, 5180 km).
We observed temporal substructuring of Co. hominivorax subpopulations in the
southern region of Uruguay, specifically in Joaquín Suarez and Colonia. These populations
exhibit significant differences in heterozygosity within the same locations across different
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years (HEfor UJ3 = 0.66 and UJ4 = 0.82; UL3 = 0.77 and UL4 = 0.63), with greater
differentiation (FST pairwise) compared to other locations (FST pairwise UJ3 and UJ4 = 0.074;
UL3 and UL4 = 0.095).
Discussion
According to our initial hypothesis, based on previous studies on Co. hominivorax
populations, we anticipated a high genetic differentiation in subpopulations with a central
distribution, structured according to an isolation-by-distance model. Additionally, peripheral
populations were expected to exhibit reduced genetic variability compared to central
populations. However, we found that: (1) genetic variability remains consistent across the
species’ distribution extremes; (2) population differentiation is low, and (3) subpopulations
do not follow an isolation-by-distance model; (4) temporal substructuring in peripheral
populations.
Our results contrasted from those obtained by Infante-Vargas and Azeredo-Espin [38],
and Infante-Malachias [37]. Using mtDNA, RFLP markers, RAPD, and Isozymes, they
indicated high levels of genetic differentiation with reduced gene flow among Brazilian Co.
hominivorax populations Nevertheless, Lyra et al. [42] found lower but significant
differentiation among mainland populations using mtDNA polymerase chain
reaction-restricted fragment length polymorphism (PCR-RFLP; фST = 0.039, p < 0.05).
Discrepancies among these studies might be due to differences in molecular markers and
sampled locations.
The main difference between our study and the previous ones lies in the sampling
methodology. Due to difficulties in using artificial attractants to collect Co. hominivorax
adults in traps, Lyra et al. [42] and we collected larvae directly from infested animals,
ensuring a wider sampling collection. However, this approach may generate biases if
individuals from a single oviposition are overrepresented. This bias might occur due to larvae
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from a single open wound are from an oviposition by only one or a few females [9]. If all
individuals from a wound are collected, the sample size may be adequate at 20-30
individuals, but they may be from one or two families, which would bias the results. In
previous studies, between 16 and 27 individuals were used per wound. Individuals were
sampled from one, two, three, or nine wounds in each analyzed subpopulation. The use of
many individuals per wound associated with sampling in only a few wounds may have
generated deviations due to the increase in the frequency of rare haplotypes or alleles and the
failure to observe all possible haplotypes or genotypes in each locus. To mitigate this, we
implemented the criteria for selecting larvae from different ovipositions, broadening our
sampling across wounds from different hosts. In addition, larval stage and mitochondrial
haplotype from previous studies [40,42] were used to differentiate individuals that were on
the same wound but came from different ovipositions.
In the current analysis using all loci together, all samples exhibited an excess of
homozygotes compared to the expected equilibrium, potentially attributable to different
factors, including the Wahlund effect, null alleles, and selection pressure [58]. Firstly, these
deviations can stem from the Wahlund effect [59], which arises when a population is not
homogeneous but consists of subpopulations with varying allele frequencies [60]. When
samples from these subpopulations are mixed, it can lead to an excess of homozygotes
compared to what would be expected if all individuals were mating randomly [61,62]. This
was confirmed by the positive correlation observed between FIS and FST.
The excess of homozygotes could also be due to the presence of null alleles, which
might lead to an underestimation of heterozygosity [63]. Although null alleles may be present
at some loci, this does not seem to be the only one responsible for the excess of homozygotes
since the deviations were generally observed in at least one locus in all subpopulations.
Furthermore, there was no change in these results with the exclusion of the locus with the
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highest null alleles estimate (CH10, data not shown). Once again, the occurrence of
demographic changes appears as a possible explanation for the results.
Lastly, the excess of homozygotes may be due to selection acting on one or more loci,
if one or more loci are under selection pressure, it can lead to an excess of homozygotes if
certain alleles are being favored over others [64]. The control of Co. hominivorax with
insecticides has caused rapid selection for resistance in some populations [21–23]. This
resistance is multilocus, potentially affecting the entire genome and influencing a wide range
of genetic traits and interactions [23]. Baqir and Ab Majid [65] highlight selection pressure in
the population genetic structure of tropical bugs in Iraq. Their study reveals bottleneck events
with an excess of homozygosity, suggesting a decrease in the effective size of the bug
population due to control activities and the speed at which the population recovered,
emphasizing the role of selection in these populations [65].
Further investigation is needed to elucidate the underlying cause of the observed
excess of homozygotes. Understanding this phenomenon is crucial for accurately interpreting
population genetic dynamics. Therefore, our interpretation of Co. hominivorax population
structure and dynamics focus on two main areas: (A) historical processes and (B)
contemporary demographic processes.
A. Historical Processes
Estimates of gene flow between species populations are generally based on differentiation
indices such as FST estimates [66,67]. This approach is based on a few assumptions. The most
crucial one, and the one most likely to be violated in many natural populations, is the balance
between mutation, migration, and drift. Violation of balance is critical in pest species
(organisms that depend on humans and their resources as causative agents of human disease,
domestic animals, and cultivated plants), whose effective population sizes are generally high
and whose demographic history is recent [68–70].
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Lehmann et al. [71] conducted a study of the distribution of genetic variability of
isozyme and microsatellite loci among populations of An. gambiae from Kenya and Senegal,
Africa. These authors found low population differentiation (FST = 0.016) and estimated a high
level of gene flow between populations (Nm > 7.7) separated by more than 6000 km.
Besansky et al. [72], analyzing sequence data from a mitochondrial region ND5 gene (665
bp) from seven villages in Kenya and three in Senegal, found homogeneity between the
subpopulations of the two countries (FST = 0.085). Consequently, the estimates of gene flow
were very high (Nm = 5.4). Similar results were obtained for Anopheles arabiensis, whose
populations separated by 7000 km were also homogeneous (FST = 0.044, Nm = 10.8).
Donelly et al. [68] analyzed the genetic variability of 18 microsatellite loci in African
populations of An. arabiensis and An. gambiae. They verified that the analyzed populations
were not in mutation-migration-drift equilibrium. This observation was consistent with the
detection in previous studies of low levels of genetic differentiation across the distribution of
these two species. Similar patterns were observed in Ce. capitata [73], Ceratitis rosa and
Ceratitis flavicentris [74], in Drosophila melanogaster [75] and in populations of dung
beetles of the species Aphodius fossor that uses bovine manure to feed the larvae,
demonstrating a high dependence on livestock [76], such as Co. hominivorax.
A parallel can be drawn between these studies and the present analysis of Co.
hominivorax populations. In this study, a deviation from the Hardy-Weinberg equilibrium was
observed in almost every test performed. Likewise, a high number of significant linkage
disequilibrium tests were observed. These results are accompanied by a low genetic structure
in terms of FST estimates and the absence of isolation by distance. For Co. hominivorax, these
results could also be explained by a recent population expansion. This increase probably
accompanied the introduction of cattle and livestock in South America after the beginning of
the colonization of the continent.
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The first record of the introduction of cattle in South America is from 1524 in the
present-day Colombia [77], and livestock is registered as standard practice from 1614 when it
became a population factor in the countryside region. Two scenarios can be considered
regarding the Co. hominivorax population expansion in South America. In the first one, the
species was already found in the continent before the introduction of cattle, living in small
effective populations, infesting small and medium-sized wild animals. The introduction of
intensive livestock farming represented the creation of a new niche for Co. hominivorax,
causing rapid and disorganized population growth. In the second scenario, this species was
not native to the continent, but it was introduced, becoming a problem for livestock in South
American countries.
The Cochliomyia genus is formed by four species, Co. hominivorax (Coquerel),
Cochliomyia macellaria (Fabricius), Cochliomyia aldrichi (Del Ponte), and Cochliomyia
minima (Shanon). Of these species, only Co. hominivorax is described as an obligate
ectoparasite [78]. The distribution of Co. macellaria is sympatric to Co. hominivorax.
Cochliomyia aldrichi is present in Florida (United States), Bermuda, Bahamas, Cuba, Puerto
Rico, San Salvador, the Virgin Islands, and the Cayman Islands [78]. There are records of this
species also in the Dominican Republic and Antilles [7]. The distribution of Co. minima is
more restricted, including Jamaica, Cuba, Dominican Republic, Puerto Rico, the Virgin
Islands, West Indies, and Florida [7,78]. With these observations, the radiation of the genus
could have occurred outside the South American continent, and the two species, Co.
hominivorax, and Co. macellaria could have been introduced in South America. However,
previous studies point to South America as the probable center of diversity and origin of the
screwworm fly [37]. Although previous studies have comprehensively evaluated the
dynamics and genetic structure of populations of this species outside of South America [43],
it is still premature to assert the status of Co. hominivorax as an introduced species.
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Regardless of which scenario best describes the evolutionary history of this species,
the human influence on the population structure of this livestock pest and its potential for
dispersion and adaptation is evident. Therefore, Co. hominivorax should be considered a
high-risk species when introduced into a new environment.
Generally, population genetics models ignore the complexity of biological systems.
Evolutionary estimates parameters, such as gene flow, are rarely valid because the parameters
that define these models are spatially and temporally variable and are very sensitive to
evolutionary forces. Conventional models assume that population structure and demographic
parameters such as population size and dispersion rates are uniform and constant in time and
space. Demographic and genetic balance assumptions are unrealistic and violated, as verified
in Co. hominivorax populations in South America. Cochliomyia hominivorax population
structure does not follow classical population genetics models.
Our data reveal a new scenario much more complex than what had been imagined for
Co. hominivorax. Thus, the implications for the establishment of control programs are not
intuitive. Each of the new hypotheses has distinct implications for control programs.
The recolonization after population reduction in the adverse season is accompanied by
a rapid Co. hominivorax population growth rate that may have ensured the maintenance of
genetic variability. This is consistent with the species recovery capacity that suffers a drastic
population reduction in the dry season and recovers quickly in the rainy season. This
seasonality in the screwworm fly infestation cases is observed throughout its distribution.
Another common factor among the analyzed locations is the indiscriminate use of
insecticides, which would also cause this fly’s population reduction.
No previous studies were carried out to investigate the dynamics and genetic structure
of Co. hominivorax populations prior to control programs using SIT. Therefore, it is difficult
to predict the outcome of similar programs in South America or the Caribbean islands. If the
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scenario proposed in this study is confirmed by additional investigations, it is likely that the
best strategy for controlling Co. hominivorax populations is a synchronized action with a
wide geographic range and not the way it is currently carried out through isolated initiatives.
It is also vital for the success of the control program to use an integrated system, with the
coordinated use of different control methods and the use of climatic restrictions to help
reduce the population. Only this way, we can ensure that the refugees will not serve as a
source for new recolonizations, which would compromise the effectiveness of the eradication
initiative.
Here we provide essential data on the genetic population structure for this species that,
together with information obtained from ecological studies, can be used to assess the
necessary size of the management unit and the program implementation area, providing
fundamental information for decision-making regarding the eradication of this important pest
of livestock throughout its current geographic range.
B. Contemporary Demographic Processes
Analyses of South American samples’ yielded results similar to those presented in the
Uruguayan samples [41]. The migration of adult flies between different subpopulations is not
enough to explain the low population differentiation since this would result in a positive
correlation between geographic and genetic distances.
Passive migration of larvae through infested animals could explain, in part, the
observations at a local scale. The transport of animals can occur between different farms of
the same owner or in the early stages of infestation (such as eggs or first instar larvae) with
animal trade. However, passive migration would not cause the observed patterns at the
analyzed scale for several reasons, (1) the cost of transporting animals over long distances is
very high, restricting this practice; (2) infestation in more advanced stages is easy to detect,
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which makes it challenging to trade infested animals; and (3) inspection is more restricted at
international borders.
Fountain et al. [69] used 21 microsatellite markers to investigate human-facilitated
metapopulation dynamics in Cimex lectularius, an emerging pest. They emphasize the impact
of colonizer numbers and demographic history on genetic diversity distribution. Pest control
leads to local extinctions, while human-facilitated dispersal fosters colonization, shaping
metapopulation dynamics. Founder events reduce diversity and increase genetic drift, causing
rapid population divergence. Despite low diversity within infestations, genetic differentiation
among infestations (FST = 0.59) highlights a high population structure. Colonization patterns
align with the propagule reservoir model, providing insights for bed bug infestation control.
While the acknowledgment of time's influence on the genetics of animal populations
has expanded through the inclusion of time as a variable in research and analysis [79–81], our
endeavor represents a pioneering step in integrating the concept of 'isolation by time' into
genetic analyses of Co. hominivorax populations. The results obtained here with the temporal
analysis, comparing samples in Uruguay at the same location in two consecutive years,
indicate that the local population dynamics of the screwworm fly include population
fluctuations dependent on the occurrence of migration, extinction, and recolonization.
Temporal genetic differentiation can occur when local extinction occurs, due to significant
fluctuations in population size and subsequent colonization of the empty habitat by
individuals from populations from more distant locations. Thus, the data obtained fit into a
metapopulation model. At the population level, barriers to dispersal and regional selection
play a crucial role in shaping the metapopulation configuration, thereby influencing
evolutionary dynamics [82].
A metapopulation can be defined as a set of subpopulations of a given species, with
each subpopulation occupying a different fragment of a subdivided habitat. Most species are
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naturally distributed as metapopulations [83,84]: many insects live in trees or other plants,
which are a fragment of habitat; many fish and marine invertebrates live on coral reefs; most
parasites live on hosts that are effectively fragments of habitat. In the specific case of Co.
hominivorax, there is a fragmentation of habitats in several aspects: each farm animal where
the larvae develop can be considered a fragment, as well as each livestock region, that usually
are not continuous, with areas of agricultural production that could act as a form of
fragmentation of habitat. Finally, the distribution of forests where there are sites of
aggregation of adult flies [85] is currently fragmented due to human action. Thus, according
to species distribution in fragmented habitats and the results demonstrated here, each
subpopulation of Co. hominivorax should be seen as an integral part of a metapopulation. For
other species sharing features with Co. hominivorax, such as wide distribution and pest status,
population structures have already been described using the metapopulation model. Evidence
indicates that populations of the blowfly Phormia regina exhibit pronounced temporal
structure that seems to correspond with seasonal shifts, suggesting that P. regina likely
operates within a metapopulation framework. Owings et al. [81] stress the significance of
spatiotemporal sampling in uncovering the population genetic structure in blowflies,
alongside the impact of abiotic variables on these patterns. Their study delved into the
temporal population genetic structure of nine populations of P. regina over three years,
analyzing them at six polymorphic microsatellite loci. The findings suggest a robust temporal
structure mirroring seasonal variations, indicative of a metapopulation dynamic. Molecular
variance analysis of these populations corroborated significant temporal genetic
differentiation, while further analyses revealed correlations between abiotic factors like
temperature, humidity, precipitation, and wind speed with the observed genetic subdivisions.
This population dynamics model has also been described for the Mediterranean fly,
Ceratitis capitata. In an ecological study on a 3000 km2expanse of crops. Israely et al. [86]
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analyzed spatial and temporal components influencing the distribution pattern of the fly in
Israel. In Israel, the fly recovers quickly, even with routine control and a temporary reduction
in population density. As well as Co. hominivorax,C. capitata does not resist the Israeli
winter but recolonizes this region during the favorable season from more beneficial areas in
the Mediterranean coastal areas and the Jordan River valley. The results obtained in the
temporal analysis indicated that in two of the three analyzed locations, the population
recovery in early summer would probably be a result of recolonization from other
populations and not the growth of a residual population that survived the winter. The
conclusion was that fly migration over long distances played a key role in maintaining
Mediterranean fly populations in the country. Ke et al. [87] investigated the population
dynamics and migration of the Plutella xylostella pest in overwintering regions in southern
China and Southeast Asia. They conducted samplings over two consecutive years and
constructed a population network to analyze contemporary gene flow. They discovered two
distinct swarms, with one replacing the other over time, and estimated an average migration
distance of about 1000 km, indicating large-scale migration. They observed greater migration
activity in spring than in winter, identifying source and sink regions. This rapid population
turnover and metapopulation dynamics highlight the importance of temporal sampling and
network analysis in understanding contemporary genetic patterns and effectively managing
agricultural insect pests like P. xylostella. The findings contribute to monitoring insecticide
resistance and identifying key populations for long-term sustainable management.
Another example comes from Anopheles gambiae, the main insect vector of
Plasmodium falciparum, the parasite that causes malaria. The genetic diversity of populations
of this mosquito is affected by fluctuations in temporal and geographic genetic structure. An.
gambiae populations are characterized by geographic heterogeneity, absence of random
crossing, and absence of limits for dispersion. Microgreographical studies in a village in Mali
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indicated a metapopulation structure [88]. However, two different metapopulation structures
are proposed for this species to try to explain the maintenance of local populations. In the
first scenario, local populations disappear entirely during the dry season and are
re-established in the favorable season. In the second, local populations are maintained in situ
by some form of individual aestivation or diapause in the dry season. With the return of
favorable conditions, the individuals in diapause would restore the population. With the data
obtained for this species so far, it has not yet been possible to determine which factor is most
important in the observed pattern of genetic structure.
In a grasshopper species, Schistocerca gregaria [89], a metapopulation structure
highly dependent on extinction and recolonization processes was described. This species,
together with species of the genus Locusta, represents the major pests of agriculture in desert
areas of the old world. During the recession period, this species is reduced to small, barely
detectable population sizes subject to local extinctions. However, during plague outbreaks,
there was a surprising increase in population density, creating grasshoppers’ clouds that
attacked crops in as many as 60 countries in the old world. According to the author, the
metapopulation structure, mainly the recolonization of unoccupied niches, is fundamental for
maintaining the cycles of outbreaks and population retraction of this species. Similarly
demonstrated in populations of Aedes aegypti from Brazil, Mendonça et al. [90] examined the
genetic structure and gene flow of Ae. aegypti populations in Manaus, Brazil, during periods
of high infestation and dengue outbreaks in urban neighborhoods, both during rainy and dry
seasons, using nine microsatellite loci. The results showed genetic homogeneity and
extensive gene flow during the rainy season, attributed to abundant breeding sites.
Conversely, the dry season exhibited significant genetic structure, primarily due to reduced
effective population size in some neighborhoods. Genetic bottleneck analyses indicated
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continuous population maintenance with seasonal reductions rather than severe bottlenecks.
These findings are crucial for the development of effective dengue control strategies.
Conclusions
The effective size of the Co. hominivorax metapopulation in South America is large and
dynamic, ensuring theo preservation of genetic diversity and reducing the risk of local
extinction due to processes such as inbreeding. The metapopulation has a high probability of
survival due to gene flow and recolonization, with migration between subpopulations
maintaining overall stability. This stability persists even if some subpopulations undergo local
extinctions during cold and dry periods or due to local pesticide use. Although the temporal
analysis included only a few populations over a short time period, it was sufficient to
demonstrate that populations sampled at different times are distinct. This novel finding,
supported by the metapopulation structure of the species, indicates that control strategies
should be coordinated and comprehensive. Effective and sustainable pest control must
consider the connectivity between subpopulations to optimize management efforts.
Acknowledgements
Authors are grateful to Rosangela A. Rodrigues, and Maria Salete Couto for their assistance
in the laboratory and with fly maintenance.
Funding
This work was supported by FAPESP Dimensions US-Biota São Paulo grant to T.T.T.
(2020/05636-4). This study was financed in part by the Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. K.S.S. was supported by PD
scholarship (FAPESP 2023/12670-2). L.C.B.P. was supported by Capes
(88887.816569/2023-00).T.T.T. was supported by CNPq (310906/2022-9).
Availability of data and materials
All data provided and analysed during this study are included in this article.
Authors’ contributions
AA-E and TT designed the experiments, KSS and TT analysed the data KS, LC and TT
helped draft the manuscript.
Supplementary information
Additional file 1: Table S1. Microsatellite loci polymorphism.
Additional file 2: Table S2. Genetic diversity of Co. hominivorax samples collected in
different locations in South America.
Additional file 3: Fig S1. Number of alleles.
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Table 1. Sampled locations with their respective geographical information.
Country
ID
Localities
Number of
individuals
Latitude
Longitude
Collectio
n date
(year)
Uruguay
UD3
Dayman
19
31° 33' 00S
57° 57' 00W
2003
US3
San Antonio
21
31° 24' 00S
57° 58' 00W
2003
UC3
Cerro Colorado
29
33° 52' 00S
55° 33' 00W
2003
UL3
Colonia
32
34° 28' 00S
57° 51' 00W
2003
UB3
Banãdos de
Medina
24
32° 23' 00S
54° 21' 00W
2003
UJ3
Joaquín Suarez
15
34° 44' 00S
56° 02' 00W
2003
UP3
Paso Muñoz
15
31° 27' 00S
56° 23' 00W
2003
UJ4
Joaquín Suarez
37
34° 44' 00S
56° 02' 00W
2004
UL4
Colonia
16
34° 28' 00S
57° 51' 00W
2004
US4
San Antonio
20
31° 24' 00S
57° 58' 00W
2004
UB4
Banãdos de
Medina
16
32° 23' 00S
54° 21' 00W
2004
UC4
Cerro Colorado
32
33° 52' 00S
55° 33' 00W
2004
Brazil
BPM
Pinheiro Machado
24
31° 35’ 00S
53° 23’ 00W
2005
BFV
Fagundes Varela
10
28° 56’ 00S
51° 41’ 00W
2005
BSA
Santo Antonio das
Missões
19
28° 31’ 00S
55° 14’ 00W
2004
BC
M
Carambeí
37
24° 56’ 00S
50° 03’ 00W
2005
BES
Estiva
47
22° 28’ 00S
46° 01’ 00W
2006
BSS
São Sebastião do
Paraíso
17
20° 56’ 00S
46° 59’ 00W
2004
BCG
Campo Grande
12
20° 26’ 00S
54° 37’ 00W
2004
BCR
Costa Rica
12
18° 33’ 00S
53° 08’ 00W
2002
BCA
Caiapônia
42
16° 57’ 00S
51° 49’ 00W
2005
BGO
Goianira
24
16° 29’ 00S
49° 24’ 00W
2004
BGY
Goiânia
20
16° 36’ 00S
49° 20’ 00W
2001
31
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BCO
Cocalinho
15
14° 26’ 00S
51° 47’ 00W
2004
BSM
Santa Maria das
Barreiras
46
08° 51’ 00S
49° 43’ 00W
2004
BCP
Campinas
24
22°54’20S
47°03’38W
2004
Venezuela
VBA
Barquisimeto
31
17° 18' 00S
52° 41’ 00W
2003
VM
A
Encontrados
30
9° 20' 51N
72° 11’ 20W
2004
Paraguay
PAR
Ybytymí
30
25° 46' 00S
56° 41’ 00W
2004
32
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Table 2. Tests to detect the populational reduction on the BOTTLENECK program.
ID
Mutation model
P-value
Heterozygote
Interpretation
HDef
HExc
UD3
IAM
TPM
SMM
0.99329
0.63330
0.15063
0.01709
0.39551
0.86694
excess
equilibrium
equilibrium
US3
IAM
TPM
SMM
0.86694
0.23486
0.02124
0.15063
0.78809
0.98291
equilibrium
equilibrium
deficiency
UC3
IAM
TPM
SMM
0.99597
0.78809
0.08813
0.00525
0.23486
0.92432
excess
equilibrium
equilibrium
UL3
IAM
TPM
SMM
0.99915
0.95386
0.42505
0.00122
0.05493
0.60449
excess
equilibrium
equilibrium
UB3
IAM
TPM
SMM
0.97388
0.57495
0.02612
0.03198
0.45483
0.97876
excess
equilibrium
deficiency
UJ3
IAM
TPM
SMM
0.99329
0.92432
0.25928
0.01709
0.08813
0.76514
excess
equilibrium
equilibrium
UP3
IAM
TPM
SMM
0.99475
0.78809
0.33862
0.00671
0.23486
0.68896
excess
equilibrium
equilibrium
UJ4
IAM
TPM
SMM
1.00000
0.95386
0.08813
0.00012
0.05493
0.92432
excess
equilibrium
equilibrium
UL4
IAM
TPM
SMM
0.99475
0.71533
0.15063
0.00671
0.31104
0.86694
excess
equilibrium
equilibrium
US4
IAM
TPM
SMM
0.99988
0.96802
0.25928
0.00024
0.03857
0.76514
excess
excess
equilibrium
UB4
IAM
TPM
SMM
0.99329
0.76514
0.16968
0.01709
0.25928
0.84937
excess
equilibrium
equilibrium
UC4
IAM
TPM
SMM
0.99939
0.97388
0.11670
0.00085
0.03198
0.89819
excess
excess
equilibrium
BPM
IAM
TPM
SMM
0.96802
0.42505
0.03857
0.03857
0.60449
0.96802
excess
equilibrium
deficiency
BFV
IAM
TPM
SMM
0.98291
0.83032
0.48486
0.02124
0.19019
0.54517
excess
equilibrium
equilibrium
BSA
IAM
TPM
SMM
0.80981
0.16968
0.00171
0.21191
0.84937
0.99878
equilibrium
equilibrium
deficiency
BCM
IAM
TPM
SMM
0.99695
0.71533
0.03857
0.00403
0.31104
0.96802
excess
equilibrium
deficiency
BES
IAM
TPM
SMM
0.99878
0.54517
0.00305
0.00171
0.48486
0.99768
excess
equilibrium
deficiency
BSS
IAM
TPM
SMM
0.91187
0.36670
0.04614
0.10181
0.66138
0.96143
equilibrium
equilibrium
deficiency
BCG
IAM
TPM
SMM
0.99329
0.84937
0.15063
0.01709
0.16968
0.86694
excess
equilibrium
equilibrium
BCR
IAM
TPM
SMM
0.99829
0.96802
0.68896
0.00232
0.03857
0.33862
excess
excess
equilibrium
33
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BCA
IAM
TPM
SMM
0.99878
0.78809
0.00305
0.00171
0.23486
0.99768
excess
equilibrium
deficiency
BGO
IAM
TPM
SMM
0.99475
0.95386
0.21191
0.00671
0.05493
0.80981
excess
equilibrium
equilibrium
BGY
IAM
TPM
SMM
0.99988
0.63330
0.01709
0.00024
0.39551
0.99329
excess
equilibrium
deficiency
BCO
IAM
TPM
SMM
0.99963
0.98291
0.42505
0.00061
0.02124
0.60449
excess
excess
equilibrium
BSM
IAM
TPM
SMM
0.99988
0.91187
0.08813
0.00024
0.10181
0.92432
excess
equilibrium
equilibrium
BCP
IAM
TPM
SMM
0.99963
0.99329
0.76514
0.00061
0.01709
0.25928
excess
excess
equilibrium
VBA
IAM
TPM
SMM
0.96802
0.78809
0.05493
0.03857
0.23486
0.95386
excess
equilibrium
equilibrium
VMA
IAM
TPM
SMM
0.99768
0.80981
0.33862
0.00305
0.21191
0.68896
excess
equilibrium
equilibrium
PRA
IAM
TPM
SMM
0.99988
0.99939
0.93530
0.00024
0.00085
0.07568
excess
excess
equilibrium
34
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Figure captions
Figure 1. Location of samples of Co. hominivorax in South America, with heterozygosity
ranging from 0.621 in Joaquín Suarez (UJ3) to 0.778 in Caiapônia (BCA).
Figure 2. Matrix of pairwise FST estimates between subpopulations pairs a) Group 1 b)
Group 2, c) Group 3, “x”, not statistically significant FST pairwise.
Figure 3. Test for isolation by distance (IBD). Linear regression of FST /(1- FST) between
population pairs against the natural logarithm of geographical distances between population
pairs, both not significant. (a) group 1 subpopulation; (b) group 2 subpopulation.
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Fig.1
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Fig.2
Fig.3
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