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
SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE2652
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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):
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��, �� = 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
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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
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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)
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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)
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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.
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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
© 2015 Macmillan Publishers Limited. All rights reserved
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
Figure S1: Scatter plot of Hindcast modelled mean yearly pollen counts versus
, 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,
Figure S1: Scatter plot of Hindcast modelled mean yearly pollen counts versus
, 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
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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
2050 under RCP8.5 climatic scenario for the reference seed dispersal scenario.
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
2050 under RCP8.5 climatic scenario for the reference seed dispersal scenario.
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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
future climate (RCP8.5 in 2050) using historical pollen production (upper panel) and for
current climate using future production (lower panel) considering no ragweed Invasion (I0
scenario). Right panels: absolute differences between average pollen concentrations in the left
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
future climate (RCP8.5 in 2050) using historical pollen production (upper panel) and for
ing no ragweed Invasion (I0
scenario). Right panels: absolute differences between average pollen concentrations in the left
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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.
13
Figure S5: Impact of future CO2 concentrations and precipitations on pollen production.
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
ariables and versus (right panel) pollen production calculated using RCP8.5
climatic scenario and current precipitations.
concentrations and precipitations on pollen production.
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 CO2 for 2050 and
ariables and versus (right panel) pollen production calculated using RCP8.5
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