Predicting impact of a biocontrol agent: integrating distribution modeling with climate-dependent vital rates

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Supporting Information. Benno Augustinus, Yan Sun, Carine Beuchat, Urs Schaffner, Heinz

Müller-Schärer. 2019. Predicting impact of a biocontrol agent: integrating distribution modeling with

climate-dependent vital rates. Ecological Applications.

Appendix S1: Temperature and relative humidity (RH) treatments of the laboratory experiments

Fig. S1: Hourly average temperature measured in the field in Hodmezuvasarhely (Hungary) and hourly

temperature programmed in the laboratory experiment. Field data collected between12-31 August 2016.

0 5 10 15 20 25 30 35 40 45 T e m p e ra tu re in ° C

Average Temperatures field vs. lab

Field Treatment

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Fig. S2: Hourly average relative humidity (RH) measured in the field in Hodmezuvasarhely (Hungary) and

hourly RH programmed in the laboratory experiment in Treatment '0' (the driest treatment).

Fig. S3: RH treatments programmed for the laboratory experiments, ranging from “Treatment 0” (see

above; Annex 1 Fig. S2) to “Treatment 24” (+24% RH).

0 10 20 30 40 50 60 70 80 0 :0 0 1 :0 0 2 :0 0 3 :0 0 4 :0 0 5 :0 0 6 :0 0 7 :0 0 8 :0 0 9 :0 0 1 0 :0 0 1 1 :0 0 1 2 :0 0 1 3 :0 0 1 4 :0 0 1 5 :0 0 1 6 :0 0 1 7 :0 0 1 8 :0 0 1 9 :0 0 2 0 :0 0 2 1 :0 0 2 2 :0 0 2 3 :0 0 R e la tiv e h u m id it y in %

Average RH field vs. lab

Field Treatment '0' 0 10 20 30 40 50 60 70 80 90 100 Re la tive hu m id ity in % Treatment 0 Treatment 7 Treatment 14 Treatment 17 Treatment 20 Treatment 24

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Supporting Information. Benno Augustinus, Yan Sun, Carine Beuchat, Urs Schaffner, Heinz Müller-Schärer. 2019.

Predicting impact of a biocontrol agent: integrating distribution modeling with climate-dependent vital rates. Ecological

Applications.

Appendix S2: Supplementary figures of various models

Fig. S1. Standard error (SE) of number of generations of Ophraella communa, based on 1000 bootstrap estimates of CDD (eqn. 2),

across the European range climatically suitable for both Ambrosia artemisiifolia and O. communa. Colors represent different levels

of SEs.

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Fig. S2. Monthly relative humidity and average relative humidity (April-August) in Europe. Colors indicate different percent

relative humidity (RH).

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Fig. S3. Monthly hatching success and average hatching success (April-August) in Europe. Colors indicate different hatching

rates.

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Fig. S4. Above: standard error (SE) of populations density of Ophraella communa (based on average relative humidity and

monthly relative temperature, Model II, eqn. 5); colors indicate different levels of SEs; below: an example of the 1000 bootstrap

replicates of population density predictions at one pixel.

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Fig. S5. Populations density of Ophraella communa (based on monthly relative humidity and monthly relative temperature, Model

I, eqn. 4). Grey color indicates the area not suitable for both A. artemisiifolia and O. communa; green color indicates the area

suitable for Ambrosia artemisiifolia but not for O. communa; reddish colors indicate the area suitable for both A. artemisiifolia and

O. communa: the darker the reddish color, the higher the density of O. communa.

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Fig. S6. Correlation between population density Ophraella communa calculated based on monthly climatic data (temperature and

relative humidly) from Model I (eqn. 4) and based on average climate data from Model II (eqn. 5) for the suitable area of both

Ambrosia artemisiifolia and O. communa in Europe.

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Fig. S7. Correlation between population density of Ophraella communa calculated based on monthly climatic data (temperature

and relative humidly) from Model I (eqn. 4) and suitability values derived from SDM (Sun et al. 2017) for the suitable area of both

Ambrosia artemisiifolia and O. communa in Europe.

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Fig. S8. Heatmap of population density of Ophraella communa (calculated based on average climatic data) from Model II (eqn. 5).

Colors indicate different levels the population density.

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Supporting Information. Benno Augustinus, Yan Sun, Carine Beuchat, Urs Schaffner, Heinz

Müller-Schärer. 2019. Predicting impact of a biocontrol agent: integrating distribution modeling with

climate-dependent vital rates. Ecological Applications.

Appendix S3 : Results of statistical analyses of the laboratory experiments

Outputs of the generalized linear models conducted to assess effect of low relative humidity on egg

hatching.

Effect of RH treatment on egg hatching rate:

## Generalized linear mixed model fit by maximum likelihood (Laplace ## Approximation) [glmerMod]

## Family: binomial ( logit ) ## Formula:

## cbind(Total_hatched, eggs_counted) ~ treatment + (1 | laying_date) + ## (1 | Incubator)

## Data: RH_hatching_all ##

## AIC BIC logLik deviance df.resid ## 839.2 850.5 -415.6 831.2 119 ##

## Scaled residuals:

## Min 1Q Median 3Q Max ## -3.1246 -1.6284 -0.0869 0.7128 5.3425 ##

## Random effects:

## Groups Name Variance Std.Dev. ## laying_date (Intercept) 0.09289 0.3048 ## Incubator (Intercept) 0.01343 0.1159

## Number of obs: 123, groups: laying_date, 23; Incubator, 2 ##

## Fixed effects:

## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -1.697835 0.137211 -12.37 <2e-16 *** ## treatment 0.053169 0.006076 8.75 <2e-16 *** ## ---

## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##

## Correlation of Fixed Effects:

## (Intr)

## treatment -0.492

Effect of RH treatment on time until hatching:

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## Family: poisson ( log ) ## Formula:

## (First_day_hatching) ~ treatment + (1 | Incubator) + (1 | laying_date) ## Data: RH_hatching_all

##

## Min 1Q Median 3Q Max ## -1.32987 -0.17184 0.05599 0.21842 1.26263 ##

## Random effects:

## Groups Name Variance Std.Dev. ## laying_date (Intercept) 0 0 ## Incubator (Intercept) 0 0

## Fixed effects:

## Estimate Std. Error z value Pr(>|z|) ## (Intercept) 2.059647 0.076428 26.949 <2e-16 *** ## treatment -0.002870 0.004641 -0.619 0.536 ## ---

## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##

## Correlation of Fixed Effects: ## (Intr)

## treatment -0.859

Effect of RH treatment on hatching interval:

## Family: poisson ( log ) ## Formula:

## (hatching_interval) ~ treatment + (1 | Incubator) + (1 | laying_date) ## Data: RH_hatching_all

##

## Min 1Q Median 3Q Max ## -1.5694 -0.5428 -0.2765 0.4623 3.7584 ##

## Random effects:

## Groups Name Variance Std.Dev. ## laying_date (Intercept) 8.016e-02 2.831e-01 ## Incubator (Intercept) 6.929e-10 2.632e-05

## Fixed effects:

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## (Intercept) 0.62894 0.17927 3.508 0.000451 *** ## treatment -0.01348 0.01127 -1.197 0.231500 ## ---

## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##

## treatment -0.760

Variation in batch size among RH treatments (to control for a bias among treatments):

## Family: poisson ( log )

## Formula: (eggs_counted) ~ treatment + (1 | Incubator) + (1 | laying_date) ## Data: RH_hatching_all

##

## Min 1Q Median 3Q Max ## -2.8290 -1.0167 -0.1309 0.8030 4.9139 ##

## Random effects:

## Groups Name Variance Std.Dev. ## laying_date (Intercept) 0.05908 0.2431 ## Incubator (Intercept) 0.00000 0.0000

## Fixed effects:

## Estimate Std. Error z value Pr(>|z|) ## (Intercept) 3.001e+00 6.389e-02 46.969 <2e-16 *** ## treatment -7.928e-05 2.952e-03 -0.027 0.979 ## ---

## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ##