Mycobacterium ulcerans dynamics in aquatic ecosystems are driven by a complex interplay of abiotic and biotic factors

  1. Andrés Garchitorena  Is a corresponding author
  2. Jean-François Guégan
  3. Lucas Léger
  4. Sara Eyangoh
  5. Laurent Marsollier
  6. Benjamin Roche
  1. Maladies Infectieuses et Vecteurs: Ecologie, Génétique, France
  2. Ecole des Hautes Etudes en Santé Publique, France
  3. ecoHEALTH Initiative, Canada
  4. Réseau International des Instituts Pasteur, Cameroon
  5. Institut National de la Recherche Médicale U892 (INSERM) et CNRS U6299, équipe 7, France
  6. International Center for Mathematical and Computational Modelling of Complex Systems (UMI IRD/UPMC UMMISCO), France
3 figures and 6 tables

Figures

Figure 1 with 1 supplement
Link between the seasonal effect for M. ulcerans and the rainfall dynamics in Akonolinga.

(A) Represents the monthly values for the seasonal effect (red), the mean rainfall for the period under study and the 3-month cumulative rainfall (blue). (B) Shows a clear linear relationship between the values of the seasonal effect and the 3-month cumulative rainfall. A graphical representation of the different seasonal effects tested can be found in Figure 1—figure supplement 1.

https://doi.org/10.7554/eLife.07616.006
Figure 1—figure supplement 1
Values for the different seasonal effects tested in the statistical models.

The seasonal effect was tested through sin (A) and cosine (B) functions with frequencies of 12 and 4 months (solid and dashed lines, respectively).

https://doi.org/10.7554/eLife.07616.007
Impact of water flow on physico-chemical characteristics of the water and M. ulcerans prevalence in aquatic communities (Bankim).

(A) Links between water conditions in the first two principal components obtained through principal component analysis (PCA). Comp.1, explaining more than 50% of the variation in physico-chemical conditions in Bankim, reveals that ecosystems with lower water flows have less dissolved oxygen, more acidic pH, and higher temperature. (B) MU positivity in each type of ecosystem as described by the first component of the PCA, which takes into account variations in all physico-chemical characteristics (each category has equal number of points and increasing values of Comp.1). Stagnant ecosystems in Bankim have higher MU positivity than lentic, and these have in turn higher MU positivity than lotic ecosystems. (C) Difference in values for the various water conditions in MU positive and MU negative sites in Bankim. As a result of the association of water flow with the other physico-chemical conditions, similar patterns for MU positivity can be observed for most abiotic conditions.

https://doi.org/10.7554/eLife.07616.008
Distribution of relevant biotic and abiotic variables for Akonolinga and Bankim.

For the construction of histograms (AD), the relative frequency of the variable within each region is normalized by dividing each frequency by its maximum frequency. It can be noted that the distribution of pH is radically different for both regions, with much more acidic pH in aquatic environments from Akonolinga. For the community composition (E and F), the area an order has in the pie chart is proportional to the mean relative abundance of the order for all sites and months for each region. Only orders representing more than 1% of the overall community are labelled.

https://doi.org/10.7554/eLife.07616.009

Tables

Table 1

Description of environments defined by principal components analysis (PCA) of physico-chemical parameters

https://doi.org/10.7554/eLife.07616.003
AkonolingaBankim
PC1PC2PC3PC4PC1PC2PC3PC4
Variance explained0.470.310.140.090.590.220.130.07
Loadings
pH−0.330.7−0.470.41−0.510.51−0.430.55
 Dissolved oxygen−0.60.310.31−0.67−0.570.330.11−0.75
 Water flow−0.59−0.320.460.59−0.51−0.290.720.36
 Temperature0.430.550.690.190.40.740.530.1
  1. Separate PCA was performed for Akonolinga and Bankim, and only the most potentially relevant parameters were included.

Table 2

Results from multi-model selection for Akonolinga (12 months of sampling)

https://doi.org/10.7554/eLife.07616.004
VariableAvg. effect (b)Uncond. SELower CLUpper CLRelative importance (wi)Nb. models
(Intercept)2.714.04−5.2110.631.0039
Seasonality
Sine (2pi*Month/12)0.360.160.040.671.0039
 Sine (2pi*Month/4)
 Cosine (2pi*Month/12)
 Cosine (2pi*Month/4)
Physico-chemical parameters
pH7.152.342.5611.730.4417
 Flow
 Temperature
 Dissolved oxygen
 Conductivity
 Iron
Physico-chemical parameters (PCA)
PC20.490.140.210.760.5622
 PC1
 PC3
Community
Abundance−0.710.18−1.07−0.351.0039
 Shannon
Aquatic taxa (%)
Gastropoda−0.580.17−0.92−0.241.0039
 Oligochaeta (Presence)0.400.28−0.140.950.9236
 Odonata0.080.15−0.210.370.8734
 Hydracarina0.190.29−0.390.760.8533
 Trichoptera−0.010.16−0.310.300.6726
Decapoda (Presence)−1.100.38−1.84−0.350.5923
 Hirudinea (Presence)0.480.26−0.020.990.5923
 Coleoptera0.240.20−0.150.630.5421
Hemiptera−0.540.21−0.94−0.130.5421
Anura−0.410.16−0.73−0.090.4116
 Ephemeroptera0.070.15−0.210.360.218
 Diptera−0.070.15−0.380.230.104
  1. Variables within each category are ordered by their relative importance. Variables with their 95% confidence interval (CI) with the same sign are represented in bold. Rare aquatic taxa are introduced in the model as Presence/Absence variables, while relative abundance is used for more abundant taxa.

Table 3

Results from multi-model selection for Bankim (4 months of sampling)

https://doi.org/10.7554/eLife.07616.005
VariableAvg. effect (b)Uncond. SELower CLUpper CLRelative importance (wi)Nb. Models
(Intercept)−13.984.13−22.07−5.891.00100
Seasonality
 Sine (2pi*Month/12)
 Sine (2pi*Month/4)
 Cosine (2pi*Month/12)
 Cosine (2pi*Month/4)
Physico-chemical parameters
Water flow (lentic)−1.800.63−3.04−0.561.00100
Water flow (lotic)−3.630.88−5.35−1.911.00100
pH
 Temperature
 Dissolved oxygen
 Conductivity
 Iron
Physico-chemical parameters (PCA)
 PC30.440.39−0.331.210.077
 PC20.340.41−0.471.150.033
PC10.670.330.031.320.011
Community
 Abundance0.860.47−0.061.791.00100
Shannon4.291.211.936.661.00100
Aquatic taxa (%)
 Gastropoda−0.390.30−0.970.180.9090
 Anura−0.540.37−1.260.190.8989
 Trichoptera−0.050.64−1.301.200.8989
 Odonata−0.050.30−0.630.530.8787
 Fish−0.890.56−1.980.200.8686
 Coleoptera−0.040.35−0.730.650.8484
 Diptera0.700.49−0.261.660.8484
 Hirudinea (Presence)−0.410.43−1.250.440.6969
Hydracarina−1.420.55−2.50−0.330.5858
 Decapoda (Presence)1.761.23−0.664.170.5353
 Hemiptera−0.070.33−0.720.570.2222
 Oligochaeta (Presence)−0.020.48−0.960.920.1414
Ephemeroptera−0.840.25−1.33−0.350.1313
  1. Variables within each category are ordered by their relative importance. Variables with their 95% CI with the same sign are represented in bold. Rare aquatic taxa are introduced in the model as Presence/Absence variables, while relative abundance is used for more abundant taxa. Results for lentic and lotic ecosystems represent the decrease in MU respective to stagnant ecosystems.

Table 4

Description of explanatory variables from our environmental data set and their usage in the statistical model

https://doi.org/10.7554/eLife.07616.010
VariableMinMaxMedianProp. zerosProp. NAsUsage
Physico-chemical parameters
 Temperature20.930.22300Raw
pH4.57.15.500Log
 Dissolved oxygen0.017.6200Log
 Conductivity10.2110.622.700Log
 Water flow00.50.030.340.01Categorical
 Turbidity2250500.190.19Removed
 Iron01000Categorical
 Phosphates02500.070.07Removed
 Sulphates06000.220.22Removed
Aquatic community
 Abondance4610,591686.500Log
 Shannon0.352.341.700Raw
Aquatic taxa (%)
 Fish00.3200.320AquaticLog
 Anura00.5400.330AquaticLog
 Gastropoda00.800.370AquaticLog
 Bivalvia00.1300.910AquaticRemoved
 Araneae00.30.010.010TerrestrialRemoved
 Decapoda00.5900.680AquaticDichotomous
 Odonata00.540.110.020AquaticLog
 Ephemeroptera00.780.160.030AquaticLog
 Hemiptera00.410.0800AquaticLog
 Trichoptera00.1900.350AquaticLog
 Lepidoptera00.1200.440TerrestrialRemoved
 Plecoptera00.0100.920AquaticRemoved
 Oligochaeta00.1800.690AquaticDichotomous
 Hirudinea00.4500.580AquaticDichotomous
 Coleoptera0.010.940.100AquaticLog
 Diptera00.790.1500AquaticLog
 Hydracarina00.1100.310AquaticLog
 Collembola00.0600.40TerrestrialRemoved
 Cladocera00.2400.470.63AquaticRemoved
Appendix table 1

Results of multivariate analyses for Akonolinga (12 months of sampling) and Bankim (4 months of sampling)

https://doi.org/10.7554/eLife.07616.011
VariableAkonolinga (n = 183)Bankim (n = 61)
EffectStd. errorp-valueEffectStd. errorp-value
Model AIC400.7182.7
Variance of random effect0.201.77
(Intercept)−12.564.40<0.001−7.401.97<0.001
Seasonality
 Sine(2pi*Month/12)0.340.140.02
 Sine(2pi*Month/4)
 Cos(2pi*Month/12)
 Cos(2pi*Month/4)
Physico-chemical parameters
 Temperature
pH8.632.44<0.001
 Dissolved oxygen
 Conductivity
 Iron
 Water flow (Lentic)−2.100.47<0.001
 Water flow (Lotic)−3.180.69<0.001
Physico-chemical parameters (PCA)
 PC1
 PC2
 PC3
Community
 Abundance−0.640.17<0.001
 Shannon4.160.97<0.001
Orders (%)
 Fish−1.620.35<0.001
 Anura−0.340.140.02−0.840.320.01
 Gastropoda−0.640.16<0.001
 Decapoda (presence)−1.370.37<0.001
 Odonata
 Ephemeroptera−0.940.21<0.001
 Hemiptera−0.470.200.02
 Tricoptera
 Oligochaeta (presence)
 Hirudinea (presence)0.590.230.01
 Coleoptera
 Diptera1.080.36<0.001
 Hydracarine−1.580.49<0.001
  1. The models used are Binomial regressions with random effect site, selected with forward–backwards procedure (see section 1 for details).

Appendix table 2

Differences in community composition between Akonolinga and Bankim

https://doi.org/10.7554/eLife.07616.012
Taxonomic groupRelative abundance (%)Mann–Whitney test
AkonolingaBankim
MeanSDMeanSDp-value
Fish1.042.202.394.93<0.001
Anura2.566.622.086.730.009
Gastropoda2.707.763.419.330.449
Bivalvia0.191.080.000.030.016
Decapoda5.3612.370.902.780.010
Odonata12.729.9615.7915.030.616
Ephemeroptera21.7717.2215.5614.740.010
Hemiptera8.155.8511.848.55<0.001
Tricoptera2.624.201.232.230.011
Hirudinea1.294.762.637.310.005
Oligochaeta0.641.940.672.110.291
Coleoptera18.9820.5411.9911.370.023
Diptera17.5814.2323.6916.250.004
Hydracarine0.831.380.901.770.171
  1. For each taxon included in the statistical model, the mean and standard deviation (SD) of the relative abundance (%) for each region are given, along with the p-value of a Mann–Whitney test comparing the mean relative abundance in the two regions.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Andrés Garchitorena
  2. Jean-François Guégan
  3. Lucas Léger
  4. Sara Eyangoh
  5. Laurent Marsollier
  6. Benjamin Roche
(2015)
Mycobacterium ulcerans dynamics in aquatic ecosystems are driven by a complex interplay of abiotic and biotic factors
eLife 4:e07616.
https://doi.org/10.7554/eLife.07616