Seasonal and Spatial Environmental Influence on ...

RESEARCH ARTICLE

Seasonal and Spatial Environmental Influence on Opisthorchis viverrini Intermediate Hosts, Abundance, and Distribution: Insights on Transmission Dynamics and Sustainable Control a11111

Christina Sunyoung Kim1, Pierre Echaubard1,2,3*, Apiporn Suwannatrai4, Sasithorn Kaewkes4, Bruce A. Wilcox1,3, Banchob Sripa1* 1 WHO Collaborating Centre for Research and Control of Opisthorchiasis (Southeast Asian Liver Fluke Disease), Tropical Disease Research Laboratory, Department of Pathology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand, 2 Department of Biology, Laurentian University, Sudbury, Ontario, Canada, 3 Global Health Asia, Faculty of Public Health, Mahidol University, Bangkok, Thailand, 4 Department of Parasitology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand * [email protected] (BS); [email protected] (PE)

OPEN ACCESS Citation: Kim CS, Echaubard P, Suwannatrai A, Kaewkes S, Wilcox BA, Sripa B (2016) Seasonal and Spatial Environmental Influence on Opisthorchis viverrini Intermediate Hosts, Abundance, and Distribution: Insights on Transmission Dynamics and Sustainable Control. PLoS Negl Trop Dis 10(11): e0005121. doi:10.1371/journal.pntd.0005121 Editor: Hans-Peter Fuehrer, Vienna, AUSTRIA Received: September 4, 2015 Accepted: October 20, 2016 Published: November 23, 2016 Copyright: © 2016 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: This study was partially supported by the Higher Education Research Promotion and National Research University Project of Thailand, Office of the Higher Education Commission, through the Health Cluster (SHeP-GMS) Khon Kaen University [BS]; the Thailand Research Fund (TRF) grant number RTA 5680006 [BS]; the International Agency Research Centre (IDRC),

Abstract Background Opisthorchis viverrini (Ov) is a complex-life-cycle trematode affecting 10 million people in SEA (Southeast Asia). Human infection occurs when infected cyprinid fish are consumed raw or undercooked. Ov requires three hosts and presents two free-living parasitic stages. As a consequence Ov transmission and infection in intermediate and human hosts are strongly mediated by environmental factors and understanding how environmental variability influences intermediate host abundance is critical. The objectives of this study were 1) to document water parameters, intermediate hosts abundance and infection spatio-temporal variation, 2) to assess their causal relationships and identify windows of transmission risk.

Methodology/Principal Findings Fish and snails were collected monthly for one year at 12 sites in Lawa Lake, an Ov-endemic region of Khon Kaen Province in Northeast Thailand. Physicochemical water parameters [pH, temperature (Tp), dissolved oxygen (DO), Salinity, electrical conductivity (EC), total dissolved solid (TDS), nitrite nitrogen (NO2-N), lead (Pb), total coliform bacteria (TCB) and fecal coliform bacteria (FCB)] were measured. Multivariate analyses, linear models and kriging were used to characterize water parameter variation and its influence on host abundance and infection prevalence. We found that sampling sites could be grouped in three clusters and discriminated along a nitrogen-salinity gradient where higher levels in the lake’s southern region predicted higher Bithynia relative abundance (P 0.4) from the projections of normalized water parameter data. Heavy loadings (>0.4) help identify which original variables contribute to the construction of a given component and therefore allow us to determine a synthetic dominant trend for each component. Relationships between site clusters and Ov intermediate host abundance, species diversity and prevalence of infection. We computed a generalized linear mixed model (GLMM) to assess the influence of the independent variables “seasonality” (fixed effect), “clusters-specific combinations of environmental parameters” (fixed effect) as well as “single sampling sites” (random effect) on the following response variables: “Bsg and cyprinid fish relative abundance” (two variables), “snail and fish species diversity” (two variables), as well as “Ov infection rates in Bsg and cyprinid fish” (two variables). When relevant, we also computed general models assessing the influence of specific environmental parameters (e.g. salinity) on the responses variables. Bsg and cyprinid fish relative abundance were calculated as the proportion of Bsg and cyprinid fish individuals relative to individuals from other snail and fish species. Diversity of snails and fish per site and sampling event (time) was estimated using the Shannon diversity index (H) for which the proportion of species i relative to the total number of species (pi) is

PLOS Neglected Tropical Diseases | DOI:10.1371/journal.pntd.0005121 November 23, 2016

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Seasonal and Spatial Influence on Liver Fluke Transmission

calculated, and then multiplied by the natural logarithm of this proportion (lnpi). The resulting product is summed across species, and multiplied by -1 (S1 and S2 Tables). Due to the fact that our data set showed low infection rates (zero-inflated data), we computed our models using the logarithm as the (canonical) link function, and the Poisson distribution function as the assumed probability distribution of the responses. From the information we gained through CA, PCA and GLMM analyses we created maps of Lawa Lake illustrating the spatial and seasonal variability of significant water parameters in relation to snails and fish abundance as well as infection using the Universal Kriging interpolation method [38]. Our data set was fit to a spherical variogram model using inferences that were based on restricted maximum likelihood [39, 40]. The best fitting model and variogram parameters (i.e. sill, range, and nugget) were selected uniquely for each data set by comparing restricted log-likelihood values. Normality was assumed throughout, as initial computation with log-normality assumptions did not yield better interpolations. All statistical analyses were performed and graphical output created using R version 3.0.1 [41] with packages: graphics [41], cluster [42], pvclust [43], ggplot2 [44], grid [41], devtools [45], factoextra [46], plyr [47], gridExtra [48], ade4 [49], scales [50], vegan [51], FactoMineR [52], Matrix [53], lme4 [54].

Results Environmental data analysis and site characterization Cluster Analysis (CA). Cluster analysis was employed to identify groups of similar sampling sites and to explore spatial heterogeneity of the environmental water quality parameters. It generated a dendrogram, grouping the 12 sites into 3 distinct clusters at (Dlink/Dmax) x 100 < 60 (Fig 2). Cluster 1 included sites, 1, 4, 10 and 11 located along near-shore zones. Cluster 2 included sites 2, 3, 5, and 6, located in the open-water zone. Cluster 3 included sites 7, 8, 9 and 12 located in the southern region of Lawa Lake. The classifications were statistically significant (P1, explaining approximately 86% of the total variance (Table 3). PC1 expressed heavy loadings (>0.4) on EC (-0.54), TDS (-0.54) and Salinity (-0.55), indicating a ‘salinity’ dominant component since EC and TDS were highly correlated to Salinity. EC, TDS, and Salinity were all negatively correlated with the component, with decreasing Salinity values when PC1 scores increase. PC2 showed heavy loading on TCB (0.51), FCB (0.52) and DO (0.50), therefore named a ‘fecal matter pollution’ component. PC3 reported heavy loadings on pH (0.61) indicating the variation of acidity and alkalinity in the lake depending on wetland features, vegetation and seasonality; PC4 with Pb (0.73) thus influence from heavy metals; and finally PC5 illustrated variation due to NO2-N (0.84), indicating a significant impact from sources of nitrogen contamination. We observed remarkable partitioning by sampling sites and to a lesser extent by data collection time along PC1, PC2 and PC5, suggesting a directional influence of salinity, fecal matter, and nitrogen variables (Fig 3). In particular, the sampling sites located in the northernmost region of the lake exhibited higher PC1 scores, hence lower salinity related variable values as compared to sites located in the southern region of the lake, (Fig 3, green dots). Northern region sites were also mostly found associated with positive values along PC5, which suggested


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Fig 2. Cluster analysis dendrogram. doi:10.1371/journal.pntd.0005121.g002

that sites 1–6 present lower nitrogen values as compared to site 7–12. PC2, a ‘fecal matter’ dominated component indicated geographic variability in fecal matter contamination at the sites nearest to the shore (black dots). Sites 11 and 12 in the southern region of the lake were remarkable for their high salinity and nitrogen values. PCA demonstrated similar results for sampling sites as the CA. The 3 clusters of sampling sites occupied different ordination space composed by PC1 and PC2. The Water quality of sites 1, 4, 10 and 11 (cluster 1) were mostly negatively correlated with Salinity (-0.5) and NO2N (-0.5) and positively correlated with FCB (0.53). Water quality of sites 2, 3, 5 and 6 (cluster 2) were also negatively correlated with Salinity, TDS and EC (-0.5) as well as demonstrated a strong positive relationship with pH (0.64) and DO (0.59). And lastly, sites 12 and 7, 8, 9 (cluster 3) presented significant negative relationships with Salinity (-0.5), NO2-N (-0.54), and FCB (-0.57). Cluster 3 also showed variation and strong negative relationship to pH (-0.61), Temperature (-0.51), and DO (-0.51), opposite to the loading results of cluster 2 that manifested positive relationships with pH and DO. Lawa Lake is largely utilized as a source of agricultural irrigation and the sites in the southern region reported NO2-N levels that exceeded 0.06 mg/L, the irrigation water quality limit recommended by the Food and Agriculture Organization (FAO) [55]. Generally, fecal coliform bacteria from ‘offshore sites’ in the northern region of the lake were less than 1,000 MPN/100 mL. However, the nearshore sites 1, 4, 9, 10 and 11 presented higher loads of fecal coliforms with their highest values occurring during the hot-dry into early hot-rainy season months, specifically from March to June: 9,200; 9,200, 3,500; 5,400; 9,200 MPN/100 mL, respectively. The MPN of fecal coliforms in these shallow nearshore sites exceeded the


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Table 2. Descriptive summary of mean and range of water parameters collected from the 12 study sties in Lawa Lake from Feb. 2013 –Jan. 2014. The highest mean values are marked by asterisks in descending order. Site 1 2 3 4 5 6 7 8 9 10 11

Sal (p.p.t.)

EC (μS/cm)

TDS (mg/L)

pH

Temp (˚C)

DO (mg/L)

NO2-N (mg/L)

Pb (mg/L)

Mean

0.3958

785.25

545

6.7783

26.15

1.7167

0.0078

0.0016

3805.8333 *

1459.8333 **

Range

0.14–0.58

290–1180

191–821

6.06–7.15

21.3–28.4

0.2–3.65

0–0.043

(-0.0032)—0.008

220–16000

11–9200

Mean

0.3533

727.25

499

7.9325

26.6917

5.4417 ***

0.0016

0.0013

477

52.5833

Range

0.14–0.48

298–983

199–680

7.06–9.14

22–29.9

2.7–8.3

0–0.006

(-0.0006)—0.006

17–2400

2–540

Mean

0.4133

839.5

583

7.05

26.9425

2.2542

0.0013

0.0018

850.5833

208.5833

Range

0.26–0.55

531–1109

359–772

6.86–7.55

23.7–30.2

0.65–4.7

0–0.005

(-0.0004)—0.01

13–5400

2–1300

Mean

0.4108

837

578

6.95

26.775

1.6458

0.0077

0.0088 ***

3255.75

2628.25 ***

Range

0.19–0.65

402–1324

272–928

6.64–7.44

25–29.1

0.3–2.8

0–0.039

0.001–0.024

4–9200

2–9200

Mean

0.415

853.25

588

7.6508

27.375

4.8625 *

0.0028

0.0019 *

889.4167

11.8333

Range

0.18–0.68

371–1388

248–978

6.93–8.78

25–30.7

1.05–6.75

0–0.007

(-0.0011)—0.005

14–9200

2–33

Mean

0.515

1069.417

735.75

7.4433

27.6083

3.9583

0.0235 *

0.0009

759.5

23.4167

Range

0.17–0.94

349–2149

233–1370

6.73–8.84

25–30.9

0.85–7.8

0–0.263

(-0.002)—0.006

4–5400

2–94

Mean

1.3283

2374

2316 **

7.2242

27.2

4.4583

0.0223

0.0008

1780.5

50.5833

Range

0.26–2.26

506–4370

578–4790

6.58–7.67

25–29.6

0.85–8.55

0–0.242

(-0.004)—0.002

33–16000

2–170

Mean

1.0583

2027.917

1545

6.9733

26.6167

2.1

0.0098

0.0005

619.83

83.4167

Range

0.34–1.85

689–3650

474–2750

6.73–7.63

20.3–29.5

0.5–6.6

0–0.082

(-0.002)—0.003

49–1600

6–350

Mean

1.575 **

2895.167 **

2308 *

7.1792

27.5583

2.7333

0.0423 **

0.0043 **

1325.75

324.9167

Range

0.43–2.37

892–4580

615–3470

6.58–7.57

25–29.7

0.25–6.1

0–0.486

(-0.0034)-0.016

14–9200

2–3500

Mean

1.025

2040.167 *

1497

7.1742

26.5167

3.3125

0.0196

0.0004

5486.6667 ***

680.6667

Range

0.39–2.08

796–4030

546–3090

6.55–7.72

20–33.3

0–5.85

0–0.168

(-0.0034)—0.003

110–24000

2–5400

Mean

1.25 *

1768.417

1532

7.1692

27.6667

3.2667

0.1312 ***

0.0012

4458.3333 **

1164.6667 **

0.22–2.55

461–4260

310–3240

6.26–7.61

23.2–32.4

0.1–6.25

0–0.664

(-0.0042)—0.01

110–24000

11–9200

5369 ***

7.2875

27.6917

5.0875 **

0.0036

0.0015

855.9167

109.1667

761–23700

6.4–9.09

20.1–36.1

1.2–9.55

0–0.011

(0.0059)—0.001

49–5400

2–540

Range 12

Mean

3.6458 *** 5714.75 ***

Range

0.53–14.4

1099–24400

TCB (MPN/100mL) FCB (MPN/100mL)

doi:10.1371/journal.pntd.0005121.t002

maximum limit of 1000 MPN of fecal coliforms per a 100 mL sample for irrigation water quality established by the WHO [56]. Table 3. Principal components (PC) from correlation matrix. Eigenvalues (Variances) PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

PC10

1.783

1.368

1.171

1.034

1.031

0.783

0.663

0.572

0.199

0.161

PC6

PC7

PC8

PC9

PC10

-0.102

0.137

0.626

-0.678

0.158

-0.258

Eigenvectors (Principal Component Loadings) PC1

PC2

Temp

-0.265

pH

PC3

PC4

0.378

0.614

-0.227

-0.195

0.311

0.331

PC5 -0.364

EC

-0.544

-0.108

0.115

-0.332

0.742

TDS

-0.543

-0.117

0.102

-0.469

-0.664

Sal

-0.545

DO

-0.174

0.817 0.496

0.376

-0.170

0.206

TCB

0.510

0.261

-0.356

0.261

FCB

0.519

0.340

-0.221

0.204

NO2-N

0.111

0.327

0.835

0.396

Pb

-0.142

0.401

0.725

0.129

-0.153

-0.687

0.161

0.646

-0.180

-0.118

-0.710

0.538

Summary of Principal Components Cumulative Proportion

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

PC9

PC10

0.318

0.505

0.642

0.749

0.855

0.917

0.961

0.993

0.997

0.100

doi:10.1371/journal.pntd.0005121.t003


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Fig 3. PCA environmental biplot. (A) PC1 –PC2 biplot, (B) PC5 –PC1 biplot, (C) PC2 –PC5 biplot. Sampling sites (dots) and variables (arrows). The green dots represent cluster 3 southern region sites; the black dots represent cluster 1 near shore sites; and the red dots represent cluster 2 deeper water sites. doi:10.1371/journal.pntd.0005121.g003


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Relationships between site environmental variation and Ov intermediate hosts abundance and diversity There was no significant difference in Bsg snail abundance by cluster groups (Table 4). However, Bsg snail abundance showed statistically significant differences by season, with the majority of Bsg snails collected during the rainy season (P