Effects of climate change and seed dispersal on airborne ...€¦ · guess, we take I=0.03. This formulation, therefore, allows estimating a first-guess distribution that accounts

1

Effects of climate change and seed dispersal on airborne ragweed pollen

loads in Europe

Lynda Hamaoui-Laguel (1, 2,*), Robert Vautard (1,*), Li Liu (3), Fabien Solmon (3), Nicolas Viovy (1), Dmitry Khvorosthyanov (4), Franz Essl (5), Isabelle Chuine (6), Augustin Colette

(2), Mikhail A. Semenov (7), Alice Schaffhauser (1), Jonathan Storkey (7), Michel Thibaudon (8), Michelle M. Epstein (9)

Affiliations:

(1) Laboratoire des Sciences du Climat et de l’Environnement, IPSL, CEA-CNRS-UVSQ, UMR8212, Gif sur Yvette, France.

(2) Institut National de l’Environnement Industriel et des Risques, Parc technologique ALATA, Verneuil en Halatte, France.

(3) Earth System Physics Section, International Centre for Theoretical Physics, Trieste, Italy.

(4) Laboratoire de Météorologie Dynamique, IPSL, CNRS, UMR8539, Palaiseau, France.

(5) Division of Conservation Biology, Vegetation and Landscape Ecology, Faculty Centre of Biodiversity, University of Vienna, Vienna, Austria.

(6) CEFE UMR 5175, CNRS - Université de Montpellier - 1919 route de Mende, 34293 Montpellier cedex 05, France

(7) Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, United Kingdom (8) Réseau National de Surveillance Aérobiologique, Brussieu, France.

(9) Department of Dermatology, Division of Immunology, Allergy and Infectious Diseases, Experimental Allergy, Medical University of Vienna, Vienna, Austria.

*Correspondence to: [email protected] ; [email protected]

Methodology and model evaluation

1. Supplementary Methods

Current ragweed density distribution: first-guess distribution

The density distribution of ragweed plants represents the number of individual per meter

square in each grid cell. Its estimation is based on 10km x 10km cell presence of ragweed as

provided by Bullock et al. (2012; ref #32). For each model grid cell (x,y), we counted the

number of 10x10 cells K(x,y) with ragweed presence as provided by the cited study. K(x,y) is

Effects of climate change and seed dispersal on airborne ragweed pollen loads in Europe

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an integer between 0 and 25 since model grid cells are 50km x 50km. However, plant density

cannot be directly derived from K(x,y), because (i) it does not account for the infestation rate,

since one observed ragweed plant is sufficient to have a 10x10km “presence” (ii) it does not

account for suitable habitat surface fraction (iii) in a number of countries, presence is absent

or considered of low quality.

For an observer looking for ragweed plants in suitable habitats, in a random manner, in a

10kmx10km cell, the probability of finding one plant scales as the infestation rate IR(x,y),

which is defined here as the ratio of the surface of suitable habitats covered by ragweed to the

surface of the suitable habitats. Thus, assuming a homogeneous distribution of surface of

suitable habitats within each grid cell and sub-cells of 10km x 10km, the mean infestation rate

IR(x,y) of the grid cell should be proportional to K(x,y). However, it is probable that observers

have a prior knowledge of where to look for ragweed in suitable areas and do not search at

random. Thus, one may suspect that they find ragweed plants more often than what the

probability predicts. We model this effect by considering that IR(x,y) actually scales as

K(x,y)r, with r greater than 1. We assumed here r=2, but we also tested r=3 and found similar

results.

For sufficient-quality presence distribution, the distribution is modelled as:

(1) ��, �� = . ��, ��. ��, ��. ��,��

where ��, �� is the surface fraction of suitable land use, here taken as the crop and urban

lands and using the CMIP5 land use classification, which scales the surface of suitable

habitats in the grid cell, and ��, �� is a climate index describing climate suitability for

ragweed at grid point. ��, �� is obtained from the suitability index ��, ��from Storkey et

al. (2014; ref #33):


3

��, �� = 0��, �� ≤ 2.4, ��, �� = 1��, �� ≥ 3.1

��, �� = ��,��!�."#.$!�." %&ℎ()*�+(

I describes the density of plants per meter square in most suitable land and climate areas, and

,��, �� = . ��, ��. ��, �� is the maximal infestation density in cell (x,y). As a first-

guess, we take I=0.03. This formulation, therefore, allows estimating a first-guess distribution

that accounts for climate and land use habitat suitability, and rate of infestation within suitable

habitat.

For low-quality presence countries or for countries where ragweed observation was not

reported, we simply approximate the infestation rate ��,�� by its average over countries

with reliable data, and replace the obtained infestation rate in Equation (S1). To weight more

near-by countries, we used a weighting in the average, which is proportional to the cube of the

inverse of the distance of the grid cells. As explained in the next section, this first-guess

distribution ��, �� is then calibrated.

Current ragweed density distribution: calibrated distributions

The first-guess distribution is then calibrated, independently for CHIMERE and RegCM. In

the two cases, a prior pollen count simulation was performed using a WRF (resp. RegCM)

hindcast simulation for CHIMERE (resp. RegCM) forced by ERA-Interim re-analysis over a

13-year period (2000-2012) for which observations were available.

For CHIMERE, stations were separated into 6 groups: France, Italy, Germany-Switzerland,

Croatia, Austria and Hungary including 19, 14, 3, 11, 2 and 2 stations respectively. Using

modelled pollen concentrations (first guess simulations) interpolated to each station, we

calculated the mean modelled and observed concentration per station. Then, the ratios

between mean observed and modelled concentrations for each group of stations are computed


4

and taken as the calibration factors. These factors are finally extrapolated over the model grid

using the Inverse-distance-weighted average method.

For RegCM, another calibration procedure was used: calibration factors were determined for

every station by minimizing the mean pollen concentration differences between observations

and simulations over the observation period, as well as minimizing the model root mean

square error calculated on daily basis. Calibration factors were then extrapolated over the

domain using standard kriging inverse distance technique. The prior density distribution is

then multiplied by the calibration factors in each grid cell to obtain, in each case, a calibrated

density.

Calibrated distributions are shown in Supplementary Fig. S2a-b. We found that the two

models have distributions that generally differ by about a factor of 2. This reveals an

uncertainty due to the dispersion models and the calibration methods used in fitting

observations. RegCM requires a higher density to simulate concentrations equivalent to those

obtained for CHIMERE. There was no a priori way to evaluate whether one model is better

than the other.

The final calibrated distribution is expressed as:

(2) �-��, �� = -. LU-��, ��. -��, ��. 0-��, ��

where - is assumed to represent the maximum value of the calibrated distribution if

suitability and infestation is maximal, and 0-��, �� represents the infestation rate (between 0

and 1). In practice, we assume that in most dense areas the infestation is maximal in all

suitable habitats. Then - is calculated as a typical value of the ratio

�- ��, �� LU-⁄ ��, �� -��, �� in this area (Pannonian plain). For CHIMERE, we take

- = 0.01 and for RegCM - = 0.02.


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Future ragweed distributions and the invasion formulation

A simplified approach of plant invasion accounting for habitat suitability in the changing

climate and land use is used, adapting the approach of Richter et al., (2012)34. Land use

projections were taken from the CMIP5 scenarios35. We model the annual seed fluxes as a

function of distance, modulated by habitat suitability. The yearly evolution of the ragweed

plant distribution is given by Dn(x,y), where n is the number of years after the start (here

assumed to be in 2005 as for the start CMIP5 future climate projections). The initial

distribution D0(x,y) is the previous calibrated distribution. The flux of seeds from grid (x’,y’)

into grid (x,y) per year from one grid cell to another is assumed to be proportional to the

inverse of the square distance.

(3) 02�� ′, � ′, �, �� = 3 456�′,�′78

9:;96�,�,�′,�′7;<9=;

where N is the number of subgrid cells equivalent to those of Richter et al. (2012)34 in one 50

km x 50 km model grid cell. Since grid cells have an area of 35 km2 in Richter et al. (2012)34,

we use N=2500/35. The characteristic distance d0 = 0.63 km is taken1, >��, �, � ′, � ′� is the

distance between the two grid cells (possibly 0). Since seeds can also spread within a grid

cell, 02��′, �′, �, �� takes a finite value, controlled by >@� �0.5B��, l being the grid cell size (50

km). In the target cell habitat suitability (depending on climate and land use changes) can

limit the distribution growth. In grid cells where maximum density

,2��, �� = -. ��2��, ��. 2��, �� for year n is reached, invasion saturates. In contrast,

invasion rate should be maximal if the cell is not infested. To reflect these properties, the

evolution of the distribution of plants is modelled as:

(4) �2<$��, �� = �2��, �� + 6,2��, �� − �2��, ��7 �1 − (!∑ F56�′,�′,�,�7G′,H′ �

We use d0 = 0.63 km for the reference scenario I1, d0 = 1.26 km for a “rapid invasion scenario


6

I2” and d0 = 0.32 km for a “slow invasion scenario I3”. Distribution results for 2050 are

shown in Fig. 2a-b and 3a-d for RCP8.5. In general the density is slightly higher with RCP8.5

than with RCP4.5 (Supplementary Fig. S2c-d-e-f) because the indexes of climate suitability

are higher for RCP8.5.

Pollen production and phenology

The ORCHIDEE model is used to calculate ragweed pollen production. ORCHIDEE is a

global land surface model defining large scale plant functional type36. It is not dedicated to

simulation of ragweed growth but sufficiently generic to be easily adaptable to simulate a

large number of species behaviour. Hence a new plant functional type has been defined to

represent the ragweed base on the generic plant functional type C3 grassland that represent

behaviour of C3 herbaceous vegetation. Therefore, main model parameters have been

calibrated (e.g maximum photosynthetic rate, phenological parameters) to represent the

typical observed ragweed biomass production.

The Phenology Modelling Plateform (PMP 5) is used to develop a process-based phenological

model for ragweed flowering which is used in ORCHIDEE to simulate ragweed start and end

of pollen season. The phenological model takes into account two phases for ragweed

development: germination (depending on 2m air temperature and soil moisture) and growth

(depending on 2m air temperature and photoperiod). In ORCHIDEE model, the end of the

pollen season is calculated using fitted PMP parameters combined to the occurrence of first

frosts which stops the flowering season.

The germination phase

Following Wang & Engel (1998)37, we consider that the germination is dependent on

temperature and soil moisture. The relationship between the rate of development of the seed

up to germination and air temperature is expressed using a non-linear sigmoid function (F1)


7

with three parameters: minimum temperature (Tmin), maximal temperature (Tmax) and optimal

temperature (Topt).

(5) 0$ = ∑ IJ� K��LM!LN=5�O6LPQR!LN=57O!�LM!LN=5�;O6LPQR!LN=57;O

, 0S9

With T = BU�2� BU K�LNVG!LN=5�6LPQR!LN=57 SW

and Td is the daily mean 2m air temperature (°C) for day d.

The relationship between the rate of development of the seed up to germination and soil

moisture is expressed using a linear function (F2).

(6) 0� = J + �1 − J� �XYX!Y��Z[!Y�� ; for swc > wp

(7) 0� = �J *\� × +*^⁄ ; for swc ≤ wp

With swc is the daily mean soil water content (g/g).

The parameters of the function are: the wilting point (wp), the field capacity (fc) and the

constant a.

The growth phase

Following Deen et al (1998)38, we consider that the main factors which affect and trigger the

growth phase are air temperature and photoperiod. The relationship between growth rate and

air temperature is represented by the function (F1). The relationship between growth rate and

photoperiod is represented by the function F3.

(8) 0# = ∑ IJ�_I�U_��`a�–��9 , ��`a�–��`@2c, 0c9

DLd is the day length for day d, DLmax = 18 and DLmin = 14 are respectively the maximum and

minimum day lengths.

Flowering date calculation

Df such as ∑ �0$4d9ef$ �g9� ×0��+*^�� = $∗ (9)


8

T1 such as ∑ �0Ya2if$9ef- �g9� ×0jk@[l��9�� = �∗ (10)

Df date of flowering; t1 date of end of germination phase and start of growing phase; t0 date

of start of germination phase; $∗ is critical value for the transition from germination to

growth. �∗ is critical value for the transition from growth to flowering.

2. Supplementary discussion

Supplementary Fig. S1a-d show a scatter plot of observed vs modelled mean yearly pollen

count sums before and after calibration. The spatial variability of pollen concentrations is

generally well represented with a Pearson correlation of 0.5 (first-guess) and 0.77 (after

calibration) for CHIMERE and 0.75 (first-guess) and 0.95 (after calibration) for REGCM

(Supplementary Fig. S1a-d). However, the calibrated models (especially CHIMERE suite)

generally overestimate the concentrations by a factor of two or more over French sites with

low pollen counts, which can be due to the load of the Roussillon station with high pollen

counts located on an infested area not representative of the grid cell. By contrast, some loaded

stations especially over Croatia are underestimated by CHIMERE but well modelled by

REGCM.

Since a spatially-varying calibration is applied, a formal validation of the method must not use

stations both for calibration and evaluation. For CHIMERE, the pollen concentrations are

validated using a 5-fold cross validation: the stations are separated randomly into 5 samples.

At each simulation (repeated five times), a sample is used to validation and the rest of stations

are used to calibration. The results for validation samples are combined and compared to

observations. The calculated Pearson correlation between observed and modelled yearly

pollen count sums is equal to 0.73 (Supplementary Fig. S1e), that is in the same order as

without cross-validation, showing the robustness of the skill measure used for the evaluation

of the modelling chains.


9

Historical simulations, which used the calibrated distributions and the historical global

climate model simulations were compared to hindcast simulations using ERA-Interim forcing,

to check whether the use of GCM driver instead of ERA-interim reanalysis forcing alters the

modelled pollen counts. We found that the historical concentrations are rather similar to

hindcast concentrations with a good correlation (0.98 for CHIMERE and 0.96 with RegCM,

Supplementary Fig. S2) but slightly lower except for North Serbia with CHIMERE and a

small area in south France with RegCM where the historical pollens concentrations are higher

than hindcast ones.

References

32. Bullock, J., et al. Assessing and controlling the spread and the effects of common ragweed

in Europe (ENV.B2/ETU/2010/0037). European Commission, Final Report (2012).

33. Storkey, J., Stratonovitch, P., Chapman, D.S., Vidotto, F. & Semenov, M.A. A process-

based approach to predicting the effect of climate change on the distribution of an invasive

allergenic plant in Europe. PLoS One, 9 (2014).

34. Richter, R., Dullinger, S., Essl, F., Leitner, M. & Vogl, M.. How to account for habitat

suitability in weed management programmes? Biol. Invasions ISSN 1387-3547, DOI

10.1007/s10530-012-0316-8 (2012).

35. Hurtt, G.C., et al. The Underpinnings of Land-use History: Three Centuries of Global

Gridded Land-Use Transitions, Wood Harvest Activity, and Resulting Secondary Lands. Glob.

Chang. Biol. 12:1208-1229 (2006).

36. Abul-Fatih H.A, Bazzaz F.A. & Hunt R. The bioplogy of Ambrosia Trifida L. III Growth

and biomass allocation. New phytol. 93, 829-838 (1979).

37. Wang, E. & Engel, T. Simulation of Phenological Development of Wheat Crops. Agric.

Syst. 58, 1-24 (1998).

38. Deen, D., Hunt, T., Swanton, C-J. Influence of temperature, photoperiod, and irradianceon


the phonological development of common ragweed (Ambrosia artemisiifolia).

555-560 (1998).

Figure S1: Scatter plot of Hindcast modelled mean yearly pollen counts versus

observations. a, b, Before calibration for CHIMERE (a) and REGCM (b).

calibration for CHIMERE (c) and REGCM (d).

indicate different station groups used in calibration process using ISO two

codes. “corr” indicates Pearson correlation coefficient.

10

the phonological development of common ragweed (Ambrosia artemisiifolia).

Supplementary Figures


, Before calibration for CHIMERE (a) and REGCM (b).

calibration for CHIMERE (c) and REGCM (d). e, The validation for CHIMERE. Colours

indicate different station groups used in calibration process using ISO two

codes. “corr” indicates Pearson correlation coefficient.

the phonological development of common ragweed (Ambrosia artemisiifolia). Weed Scie. 46,


, Before calibration for CHIMERE (a) and REGCM (b). c, d, After

, The validation for CHIMERE. Colours

indicate different station groups used in calibration process using ISO two-letter country


Figure S2: Scatter plot of historical (HIST) versus hindcast (HC) mean annual pollen

counts. a, using CHIMERE model. b, using RegCM model.

groups by country using ISO two

coefficient.

Figure S3: Ragweed density distribution (plant m

2050 under RCP8.5 climatic scenario for the reference seed dispersal scenario.

11

atter plot of historical (HIST) versus hindcast (HC) mean annual pollen

a, using CHIMERE model. b, using RegCM model. Colours indicate different station

groups by country using ISO two-letter country codes. “corr” indicates Pearson correlation

Figure S3: Ragweed density distribution (plant m-2). a, b, for current climate and


atter plot of historical (HIST) versus hindcast (HC) mean annual pollen

Colours indicate different station

letter country codes. “corr” indicates Pearson correlation

for current climate and b, c, in



Figure S4: Attribution of direct climate change impact on pollen productio

transport. Left panels: Simulated average pollen concentrations (CHIMERE model suite) for

future climate (RCP8.5 in 2050) using historical pollen production (upper panel) and for

current climate using future production (lower panel) consider

scenario). Right panels: absolute differences between average pollen concentrations in the left

panels and the historical simulation (HIST).

12

Attribution of direct climate change impact on pollen productio

eft panels: Simulated average pollen concentrations (CHIMERE model suite) for


current climate using future production (lower panel) considering no ragweed Invasion (I0


panels and the historical simulation (HIST).

Attribution of direct climate change impact on pollen production, release and

eft panels: Simulated average pollen concentrations (CHIMERE model suite) for


ing no ragweed Invasion (I0



Figure S5: Impact of future CO

Scatter plot of pollen production under RCP8.5 using ragweed current distribution (I0 for no

invasion) versus (left panel) pollen production calculated using RCP8.5 CO

current climate variables and versus (right panel) pollen production calculated using RCP8.5

climatic scenario and current precipitations.

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Figure S5: Impact of future CO2 concentrations and precipitations on pollen production.


invasion) versus (left panel) pollen production calculated using RCP8.5 CO

ariables and versus (right panel) pollen production calculated using RCP8.5

climatic scenario and current precipitations.

concentrations and precipitations on pollen production.


invasion) versus (left panel) pollen production calculated using RCP8.5 CO2 for 2050 and

ariables and versus (right panel) pollen production calculated using RCP8.5


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Effects of climate change and seed dispersal on airborne ...€¦ · guess, we take I=0.03. This formulation, therefore, allows estimating a first-guess distribution that accounts