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Biological Invasions ISSN 1387-3547 Biol InvasionsDOI 10.1007/s10530-012-0316-8

How to account for habitat suitability inweed management programmes?

R. Richter, S. Dullinger, F. Essl,M. Leitner & G. Vogl

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ORIGINAL PAPER

How to account for habitat suitability in weed managementprogrammes?

R. Richter • S. Dullinger • F. Essl •

M. Leitner • G. Vogl

Received: 21 December 2011 / Accepted: 21 August 2012

� Springer Science+Business Media B.V. 2012

Abstract Designing efficient management strate-

gies for already established invasive alien species is

challenging. Here, we ask whether environmental

suitability, as predicted by species distribution mod-

els, is a useful basis of cost-effective spatial prioriti-

zation in large-scale surveillance and eradication

programmes. We do so by means of spatially and

temporarily explicit simulations of the spread of a case

study species (Ambrosia artemisiifolia L.) in Austria

and southern Germany under different management

regimes. We ran these simulations on a contiguous

grid of the study area with each grid cell (*35 km2)

characterized by a habitat suitability value derived

from the predictions of a species distribution model.

The management regimes differed in terms of (a) a

minimum habitat suitability rank p (suitability thresh-

old) used to separate cells for surveillance from those

which are not controlled; and (b) the strategy for

selecting cells for annual campaigns from the pool

defined by p. According to the results (i.e., number of

cells infested in 2050 as well as infested on average per

year) the most efficient way to base surveillance on

suitability is to define the temporal sequence of

management according to the grid cells’ suitability

ranks. Management success declines sharply when the

suitability threshold is set too high, but only moder-

ately when it is set too low. We conclude that

accounting for environmental suitability is important

for large-scale management programmes of invasive

alien species and that species distribution models are

hence useful tools for designing such programmes.

Keywords Ambrosia artemisiifolia �Cost-efficiency � Habitat � Invasive alien species �Management � Models

Introduction

Invasive alien species are posing major threats to

ecosystems, their biodiversity and the services they

Electronic supplementary material The online version ofthis article (doi:10.1007/s10530-012-0316-8) containssupplementary material, which is available to authorized users.

R. Richter (&) � M. Leitner � G. Vogl

Faculty of Physics, University of Vienna, Strudlhofgasse

4, 1090 Vienna, Austria

e-mail: robert.richter@univie.ac.at

S. Dullinger

Vienna Institute for Nature Conservation and Analyses,

Giessergasse 6/7, 1090 Vienna, Austria

S. Dullinger

Faculty Centre of Biodiversity, University of Vienna,

Rennweg 14, 1030 Vienna, Austria

F. Essl

Environment Agency Austria, Spittelauer Lande 5, 1090

Vienna, Austria

M. Leitner

FRM-II, Technische Universitat Munchen,

Lichtenbergstr. 1, 85747 Garching, Germany

123

Biol Invasions

DOI 10.1007/s10530-012-0316-8

Author's personal copy

provide (McNeely et al. 2001; Vila et al. 2010, 2011).

In Europe, a recent exhaustive review has identified

5,789 vascular plants alien to European countries, and

this number is still growing rapidly as a consequence

of ever intensifying global trade and transport (Lamb-

don et al. 2008). Only a small proportion of these

species is likely to become naturalized (Diez et al.

2009) and even much fewer will probably prove to be a

pest (Hulme 2003). Nevertheless, the frequent lag

times between first introduction and invasive spread

suggest that the recent boost of invasive alien species

introductions will cause increasing problems for

European ecosystems and societies during the next

decades (Essl et al. 2011b).

Not surprisingly, the political profile to tackle the

spread of invasive alien species has recently risen

significantly (Hulme et al. 2009). In Europe, the

European Commission is working on a comprehen-

sive political and legal framework to mitigate

negative impacts of invasive alien species (Shine

et al. 2010). Besides more stringent measures to

prevent further introductions, such a strategy shall

also include provisions on the removal or control of

alien species already present. Designing and imple-

menting management strategies for invasive alien

species that have already become naturalized and

started to spread remains a significant challenge (see

Epanchin-Niell and Hastings 2010 for a recent

review), however, and especially so, if the current

distribution of the species is insufficiently known. In

practice, surveillance and eradication or containment

measures are usually based on ad hoc decisions

which are little coordinated among regional author-

ities (Krug et al. 2010). Such practice notwithstand-

ing, several recent studies have suggested that more

systematic management strategies could considerably

increase cost-effectiveness (e.g., Fox et al. 2009;

Krug et al. 2010; Panetta et al. 2011; Regan et al.

2011). Apart from selecting particular management

techniques, strategies of spatial prioritization of

surveillance and eradication, e.g., by concentrating

efforts on suitable habitats, seem promising (Fox

et al. 2009; Hauser and McCarthy 2009; Giljohann

et al. 2011). Such a focus on highly suitable and

hence likely infested areas appears particularly

attractive when larger-scale management designs

have to be developed under budget constraints

because it should help reducing search costs and

hence conserving resources. On the other hand, this

strategy might also be counter-productive because

invasive alien species spread could then proceed via

marginally suitable but uncontrolled areas in the

meantime.

In this paper we evaluate the utility of suitability-

based spatial prioritization in regional invasive alien

species management campaigns. We use a large-

scale plant spread model that we have recently

parametrized for reconstructing the range dynamics

of the allergenic annual weed Ambrosia artemisiifo-

lia L. in central Europe (Vogl et al. 2008; Smolik

et al. 2010). The model combines elements of species

distribution models and interacting particle systems

and simulates species spread in discrete annual steps

across a gridded landscape. We apply this model to

forecast the further spread of A. artemisiifolia across

Austria and southern Germany under different suit-

ability based management strategies whereby the

suitability of individual grid cells is measured by the

projection of species distribution models. Some

authors have based modelling of management on an

unconstrained budget (e.g., Bogich and Shea 2008),

others considered constrained budget (e.g., Giljohann

et al. 2011) and some both constrained and uncon-

strained budget (e.g., Hauser and McCarthy 2009).

For an area as large as Austria and Bavaria, an

unconstrained budget appears unrealistic; we have

hence opted to consider budget constraints. The

question we want to answer therefore is whether

there is an ‘optimal’ minimum suitability rank p (a

suitability threshold) for distinguishing the areas to

be covered by surveillance (and eradication) mea-

sures from those that do not warrant any search and

control efforts; i.e., a minimum suitability rank p that

maximizes management success under given budget

constraints. In addition, we assess whether site

suitability may also provide a guideline for the

temporal sequence of management activities. If

budgets do not allow for surveying all of the area

susceptible to invasion of a species each year, the

strategy of selecting areas for annual campaigns

might be important. In particular, following a fixed-

order sequence based on suitability ranks may be

more useful than selecting the areas at random from a

given (sub) set of grid cells, i.e., landscapes, because

controlling the most likely invasion foci first decel-

erates the species’ spread most efficiently and creates

a ‘temporal buffer’ for the surveillance of less

suitable and hence less likely infested sites.

R. Richter et al.

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Methods

Study species

Our study species, the common ragweed (Ambrosia

artemisiifolia L.), is native to North America and was

first introduced to Europe during the nineteenth

century. Gladieux et al. (2011) have recently discussed

the separate introduction to Eastern and Western

Europe. After an extended lag phase ragweed has

started to spread since the mid twentieth century and is

now a pest in several countries of Eastern Europe and

rapidly expanding in Central Europe (Chauvel et al.

2006; Brandes and Nitzsche 2007; Dullinger et al.

2009; Smolik et al. 2010), causing considerable costs

for public health systems (Reinhardt et al. 2003;

Taramarcaz et al. 2005). It particularly invades ruderal

habitats and agricultural fields (Essl et al. 2009).

Common ragweed is a wind-pollinated herb with an

annual life cycle. A single individual can produce up

to 50,000 seeds which may survive up to 35 years in

the seed bank (Brandes and Nitzsche 2007). The seeds

are relatively heavy and specialized animal dispersal

vectors are lacking in Central Europe. Seed transport

over longer distances is mostly mediated by humans,

for example, attached to vehicles or via contamination

of commercial seeds (Brandes and Nitzsche 2007).

Given the current distribution and the challenge of

eradicating a pioneer species as ragweed in a large

region, complete eradication in the total study area

(Austria and Bavaria) already seems unfeasible.

However, halting its further spread may still provide

significant gains for public health and agriculture.

Distribution data

We extracted all available records of ragweed in

Austria and adjacent southern Germany (federal state

of Bavaria) until the year 2005 from the databases of

the project Floristic Mapping of Central Europe in

Austria (Niklfeld 1998) and Bavaria (Schonfelder

1999). Starting in the late 1960s, this project system-

atically collects and compiles distribution data of all

vascular plant species on a regular raster of 3 9 5

geographical minutes (*35 km2). In addition to these

data, we searched public and private herbaria as well

as the floristic literature (see Essl et al. 2009 for

details). The documented locality of each additional

record was assigned to a grid cell of the Floristic

Mapping of Central Europe. The date (=year) of each

record was taken from the database of the Floristic

Mapping of Central Europe or by consulting the

original source or the responsible botanist. We note

that this dataset (514 infested cells) does not accu-

rately represent the spatio-temporal invasion process

because not all 4,722 grid cells of Austria and Bavaria

have been surveyed each year. Also, as our cells have a

comparatively large size, we cannot claim that cells

without recorded observation are free of A. artemis-

iifolia. Our data should therefore be interpreted as

distinguishing cells with sizable populations (which

will in the following be called infested for reasons of

brevity) from cells with no or negligible populations

(called uninfested).

To account for ragweed immigration from outside

the study area in our simulation model (see below), we

additionally compiled distribution data for those

adjacent regions, for which floristic mapping schemes

or distribution databases were available (these are

Hungary, Slovenia, and South Tyrol). These data have

also been assigned to a grid cell of the Floristic

Mapping of Central Europe.

Spread model

Smolik et al. (2010) have recently presented a ‘hybrid’

model (Thuiller et al. 2008) combining elements of

species distribution models and interacting particle

systems to simulate the range expansion of ragweed in

Austria. In brief, the model represents the study area as

a raster (grid) of cells (which is identical to the raster

of the Floristic Mapping of Central Europe in this

case) where each single cell can be in one of two

possible states, either infested or uninfested. Cell

states can change annually. The likelihood of an

uninfested cell becoming infested depends both on its

habitat suitability and on the seed influx from other

cells of the system. The habitat suitability (Fig. 1a) is

represented as a function of environmental variables,

namely a logistic regression function,

H xð Þ ¼ 1= 1þ exp �a0 � Raimi xð Þð Þð Þ; ð1Þ

where a0 is the inflexion point, ai are the components

of the parameter vector and mi(x) are the components of

the location-dependent environmental attribute vector

of cell x. In this study, we characterize environmental

conditions by four variables which have proved

significant correlates of ragweed distribution in

How to account for habitat suitability

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(a)

(b) (c)

(d) (e)

Fig. 1 (a) Distribution of habitats suitable to Ambrosiaartemisiifolia in Austria and Bavaria in the year 2005. Grid

cells correspond to the raster used in the Floristic Mapping of

Central Europe (30 9 50 geogr. minutes, *35 km2). Shades ofgrey indicate the degree of suitability, H(x), according to Eq. (1).

(b) Distribution of A. artemisiifolia in Austria and Bavaria in

2005. Black squares symbolize infested grid cells. (c) Predicted

distribution of A. artemisiifolia in Austria and Bavaria in 2050 in

a no-management scenario. (d), (e) Projected distribution of

A. artemisiifolia in Austria and Bavaria in 2050 when

management with 50 management effort units per year is

restricted to the 70 % most suitable cells (p [ 0.3) sites for

surveillance being sampled randomly each year (d) and with a

fixed-order sampling scheme (e), respectively. In (c), (d) and

(e) shades of grey indicate the probability of infestation as

predicted by our simulations. Black squares are due to

infestations that existed already in 2005 in cells not considered

for managing (with a habitat suitability rank p \ 0.3)

R. Richter et al.

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Austria in the year 2005 (Smolik et al. 2010), namely

mean annual temperature, annual precipitation sums,

summed length of major streets per grid cell and the

proportion of urban areas and agricultural fields. The

superior predictive power of this hybrid model has

been shown by Smolik et al. (2010).

The seed influx is computed via an isotropic dispersal

kernel S(d) that maps the probability that seeds produced

in cell y may arrive in cell x as a function of d = |x–y|,

the distance of the cell centres. We write r(y, t) = 1 if

the cell y is infested at time t, otherwise r(y, t) = 0. The

incoming seed flux into cell x (from all other infested

cells of the system) at time t is therefore

I x; tð Þ ¼ RyS jx� yjð Þr y; tð Þ; ð2Þ

under the assumption of equal output of seeds from

each infested cell y, in contrast to Smolik et al. (2010),

where only the influx from the nearest cell was

considered. Here we represent the dispersal kernel

S(d) by a power-law function

S dð Þ ¼ d=d0ð Þ�c ð3Þ

to accommodate the typically leptokurtic dispersal

curves of invading organisms (Kot et al. 1996), with d0

the characteristic dispersal distance.

We model the influence of habitat suitability on

colonization probability as multiplicative, i.e., we

consider the likelihood that an incoming propagule

establishes a new population proportional to the cell’s

habitat suitability. The overall probability that a

hitherto (year t) uncolonized cell becomes infested at

year t ? 1 is hence

Pðrðx; t þ 1Þ ¼ 1jrðx; tÞ ¼ 0Þ¼ 1� expð�HðxÞIðx; tÞÞ: ð4Þ

In the model we do not account for the reverse

process of local extinction, i.e., an infested cell

becoming uninfested (independent of human eradica-

tion measures), because data to parametrize such a

background extinction rate are lacking. However, due

to the longevity of ragweed’s seed bank such a

background mortality rate is probably low at the

temporal scale of a few decades.

Estimating model parameters

Starting with the year 1990 a matrix of observed

A. artemisiifolia spread was derived from the collected

ragweed records by changing the infestation status of

cell x at time t, ro(x, t), from 0 to 1 for the year the

species has first been observed in this cell and for all

subsequent years. The subscript o indicates that this is

the status of the observed infestations.

We then calculated maximum likelihood estimates

of the parameters a0, ai, d0 and c by maximizing the

product of the probabilities of all observed transitions

(either 0 ? 0 or 0 ? 1) across all cells and years. We

define K1(t) as the set of cells first infested in year t

(transition 0 ? 1) and K0(t) as the set of cells

remaining unifested in year t (transition 0 ? 0). The

likelihood function to maximize, L(a0,ai, d0, c), is

hence given by:

L a0; ai; d0; cð Þ ¼ Pt

Px2K1 tð Þ

1� exp �H xð ÞI x; tð Þð Þð Þ

Px2K0 tð Þ

exp �H xð ÞI x; tð Þð Þ: ð5Þ

Optimization was done by the Nelder-Mead method

(Nelder and Mead 1965) using GNU Octave 3.2.4.

(http://www.gnu.org/software/octave/). The resulting

numerical parameter estimates are summarized in

Table 1. In order to determine the robustness of the

obtained maximum likelihood estimates we checked

the results by doing Markov chain Monte Carlo-sim-

ulations in a Bayesian approach with uninformed

prior. The expectation values of the parameters closely

match the maximum likelihood-estimates, and the

estimated errors are also given in Table 1.

We additionally evaluated the model in an analo-

gous way as Smolik et al. (2010) by running simula-

tions to reconstruct the observed invasion of

A. artemisiifolia between the years 1990 and 2005.

The resulting value for the area under the receiver

operating characteristic curve (AUC = 0.82) indi-

cates good model performance, and the spatial auto-

correlation patterns of the observed and simulated

spread dynamics, as quantified by Moran’s I (Moran

1950), did not differ significantly (p = 0.20).

This is to our knowledge the first study where

spread and habitat parameters have been consistently

estimated as the solution of a single maximum

likelihood-problem.

Spread simulation under different management

regimes

The parametrized model was used to simulate the

further spread of A. artemisiifolia in Austria and

How to account for habitat suitability

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Bavaria until the year 2050. In these simulations

colonizations of new cells were determined by com-

paring the calculated P(r(x, t ? 1) = 1|r(x, t) = 0) of

hitherto uninfested cells to uniform random numbers

(see Smolik et al. 2010). For the sake of simplicity, we

assumed that environmental conditions remain con-

stant during this period.

The degree of ragweed infestation caused by the

simulated further spread was assessed in terms of

(a) mean annual infestations, i.e., by the mean number

of cells infested per year, and (b) the number of

infested grid cells at the end of the management period

(2050). Both measures are complementary with the

former measure being cumulative and more appropri-

ate to assess the health impact of A. artemisiifolia

(accumulated pollen load) until 2050, whereas the

latter is useful for assessing the post-2050 spread

potential.

These simulations under a no-management sce-

nario were compared with equivalent spread simula-

tions under different management protocols. In

principle, management of an established alien species

with an insufficiently known distribution consists of

two basic components: surveillance of the target

region in order to locate hitherto undetected or new

infestations, and control of known populations (Epan-

chin-Niell and Hastings 2010). In our case study,

management is done on the level of grid cells, with

control implemented as a reduction of the cell

population to a negligible level, i.e., equivalent to a

reset of the grid cells’ infestation state r(x, t) from 1 to

0. Cells reset to 0 within a particular year may

subsequently become re-colonized from other, still

infested cells.

Our management simulations evaluate the effi-

ciency of suitability-based spatial prioritization of

surveillance and eradication under budget constraints.

We quantified management costs in abstract values

(management effort units), whereby a value of 0.1

management effort units corresponds to the effort of

surveying one grid cell and a value of 1 management

effort unit to the effort necessary for successful

eradication of existing populations from such a cell

(this falls in the mid-range of reported cost-ratios of

eradication programmes (Veitch and Clout 2002;

Myers and Bazely 2003)). Simulations with other

ratios of surveillance to eradication cost, e.g., 1:3,

delivered qualitatively identical results. Eradication

costs conceptually include subsequent monitoring of

the particular patches within a cell where the species

was found in order to suppress re-emergence from the

seed bank, which obviously does not prevent the

establishment of new populations at different locali-

ties within the cell during the period of monitoring.

The annual budget was kept constant across the

entire management period (2011–2050) and was fixed

at 50 management effort units (i.e., equivalent to

surveying approximately 10 % of the study area). To

test the robustness of our results, we evaluated the

effects of varying management effort by repeating all

analyses with annual budgets of 25 and 100 manage-

ment effort units. We are aware that these assumptions

are simplifications (e.g., not taking into account

population size) and have to be specified in real world

management schemes. However, such an approach is

still useful for testing generic assumptions.

For assessing the effect of suitability-based spatial

management prioritization schemes on ragweed

spread we ordered the cells by their habitat suitability

H(x). In our simulations we then assumed that only

cells above a certain minimum habitat suitability rank

p are considered for surveillance and eradication and

explored the variation of the management success with

the choice of p. At the lower limit, i.e., when p = 0,

management measures are completely unconstrained

by habitat suitability. With increasing minimum

suitability ranks the management protocol became

progressively focussed on cells with environmentally

Table 1 Numerical values and standard deviations of the ragweed spread model parameters as estimated from the observed range

dynamics of the species in Austria and Bavaria between 1990 and 2005

d0 (km) c a0 a1 (1/km) a2 (1/�C) a3 (1/mm) a4

0.63 ± 0.14 2.02 ± 0.10 -10.30 ± 1.27 0.074 ± 0.014 0.57 ± 0.10 0.00338 ± 0.00074 1.56 ± 0.43

d0 and c are the characteristic dispersal distance and the exponent of the dispersal kernel, respectively, see Eq. (3). a0 is the inflexion

point of the logistic regression function and ai are the weights of the environmental variables used to characterize habitat suitability,

see Eq. (1); a1 to a4 give the effect of the length of main streets, mean annual temperature, annual precipitation and proportion of

urban areas and agricultural fields within the cell, respectively

R. Richter et al.

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‘optimal’ conditions: with p = 0.8, for example, only

the best suited 20 % of cells are potential management

targets.

We contrasted two different strategies of selecting

cells for annual surveillance and eradication from the

total set defined by the respective value of p, namely

random sampling and fixed-order sampling. Random

sampling meant that, each year anew, cells were drawn

at random, surveyed, and existing ragweed popula-

tions eradicated until the annual budget was con-

sumed. Fixed-order sampling implied that cells were

controlled in decreasing order of their suitability ranks

from the best suited cell to the cell with a marginal

suitability rank p. Campaigns in consecutive years

continued the series until all cells had been selected.

Subsequently, surveillance started anew following the

same order. If, for a given value of p, the fixed annual

budget was not used, we extended the area to be

surveyed (i.e., lowered the value of p) until the

available budget was spent completely.

To account for the stochastic elements in the spread

model, simulations for each value of the minimum

suitability rank p and both selection strategies were

repeated 1,000 times. Single-run results were distrib-

uted near-normally in terms of both mean annual

infestations and cells infested in 2050 and coefficients

of variation were below 10 and 15 %, respectively. In

the figures, we hence just present the mean values.

Results

Future spread in a no-management scenario

In 2005, A. artemisiifolia had sizeable populations in a

total of 514 grid cells in Austria and Bavaria (Fig. 1b),

i.e., about 11 % of all the cells in the study area.

Although the species has spread fast during the

preceding decade (Dullinger et al. 2009; Smolik

et al. 2010), this distribution is still far from an

equilibrium with the environmental conditions, i.e.,

numerous suitable cells (Fig. 1a) have not been

colonized in 2005. Simulating further spread in a no-

management scenario results in a more than threefold

increase of the number of infested cells by the year

2050 (1,682 ± 27 cells) (Fig. 1c). Here and in the

following we quote the mean value and standard

deviation of the simulated sample. This projected

range expansion corresponds to 1,147 ± 21 mean

annual infestations.

Future spread under random sampling

management protocol

Surveillance and eradication measures have a pro-

nounced effect on ragweed spread both in terms of the

mean annual infestations and of the cells infested in

2050 (Fig. 1d). Managing all cells (p = 0) with an

annual budget of 50 management effort units, the

number of infested cells in 2050 and the mean annual

infestations (Fig. 2a at p = 0) remain with 486 ± 22

and 535 ± 20, respectively, approximately at the level

of 2005 (514 cells infested).

As expected, constraining the set of cells for

management by a minimum suitability rank p (a

suitability threshold) first increases and then decreases

management success (Fig. 2a). At the minimum

suitability rank p = 0.37, under the given conditions,

the number of cells infested in 2050 drops to 359 ± 30

and mean annual infestations to 465 ± 21. At higher

values of p, the total number of cells that can be

selected for management successively declines and

these cells can hence be surveyed, and existing

populations eradicated, within a few years. In terms

of infested cells, focusing on high suitability cells is

hence relatively efficient in the first few years but

becomes detrimental later on because the ongoing

colonization dynamics in cells of lower suitability is

neglected (Figs. 3a, 4a). At the highest values of

p (p [ 0.7) management success decreases rapidly

because increasingly suitable and hence easily colo-

nized cells with suitability rank p \ 0.7 remain

uncontrolled (Fig. 2a). In summary Fig. 2a shows

that management success declines sharply when

minimum suitability rank p is set too high, but only

moderately when it is set too low.

Simulations under different management budgets

demonstrate that the most effective critical suitability

rank p is dependent on the effort that can be spent for

surveillance and eradication: the higher the budget the

lower the optimal value of p (Supplementary material).

Future spread under fixed-order sampling

management protocol

Surveying cells systematically in the order of their

suitability ranks improves management success

How to account for habitat suitability

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considerably in comparison to a random sampling

strategy (Figs. 1e, 3a, b): the number of cells

infested by 2050 decreases to 122 ± 18 and the

mean annual infestations to 338 ± 22 (Fig. 2b at

p = 0). Defining the optimal minimum suitability

rank p for constraining the cells to be surveyed

0

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mean number of infested cellsnumber of infested cells 2050

Fig. 2 Number of grid cells (30 9 50 geogr. minutes,

*35 km2) infested by Ambrosia artemisiifolia in the year

2050 (bold line) and number of mean annual infestations

(dashed line) according to simulations of ragweed spread under

different minimum habitat suitability ranks p for targeting

surveillance and eradication. In (a) simulations were run under

the assumption that cells for management are sampled randomly

from the pool defined by the minimum habitat suitability rank

p each year; in (b) cell sampling for management followed the

order of the cells’ suitability ranks. The arrows indicate the

minima of the curves

0

100

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400

500

600

2010 2020 2030 2040 2050

num

ber

of in

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ells

year

random sampling(a)

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2010 2020 2030 2040 2050

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of in

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year

fixed order sampling(a) (b)

p=0.00p=0.30p=0.50p=0.70

Fig. 3 Projected numbers of grid cells infested by Ambrosiaartemisiifolia in Austria and Bavaria during the management

period 2011–2050 for different management scenarios in terms

of minimum habitat suitability ranks p for targeting surveillance

and eradication. A given p value indicates that management is

restricted to the cells above this minimum suitability rank. In

(a) random sampling indicates that the simulations were run

under the assumption that cells for management are sampled

randomly from the pool defined by p each year; in the (b) fixed-

order sampling indicates cell sampling for management

followed the order of the cells’ suitability ranks

R. Richter et al.

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becomes more complex with a fixed-order surveil-

lance scheme.

For the first 15 years, management success is

effectively independent of a minimum suitability rank

p within a broad range of possible values because

surveillance always starts with the same set of high-

suitability cells (Fig. 3b). Subsequently, the trajecto-

ries diverge, but they do not do so in parallel. (1) for

high values of p the number of infested cells rises

again, because yet uncolonized but well-suited cells

with a p (slightly) below this minimum suitability rank

become successively occupied. (2) for intermediate

values of p the number of infested cells further

decreases and then levels off. (3) for low values of

p the number of infested cells shows a local maximum

before declining again. The maximum occurs when

management has reached the least suitable cells within

the total set defined by the minimum suitability rank

p. As these are rarely infested, the available budget is

mainly used for surveillance while, at the same time,

the species can re-colonize high-suitability cells. It is

not before management returns to these high-suitabil-

ity cells that the number of infested cells starts to

decrease again (Figs. 2b, 3b). When cells are ordered

by their suitability, the circular nature of the fixed-

order surveillance scheme produces a wave-like

infestation pattern with an amplitude that decreases

over time (Fig. 4b). The relative management success

of different minimum suitability ranks p depend on the

time horizon of management (Fig. 3b). Over the entire

40 years this optimal minimum suitability rank p tends

to approach 0, or, more precisely, management

success becomes nearly independent of the minimum

suitability rank p for values below p & 0.3 (Fig. 2b).

Like in the random sampling scenario also in the

fixed-order sampling scenario management success

declines sharply when the minimum suitability rank

p is set too high but only moderately, if at all, when it is

set too low, with the most effective critical suitability

rank p depending on the effort that can be spent for

surveillance and eradication: the higher the budgets the

lower the optimal value of p (supplementary material).

Discussion

Efficient strategies of prioritizing control areas are

urgently needed under the limited budgets available

for management of invasive alien species (Epanchin-

Niell and Hastings 2010). Common practice is largely

characterized by selective eradication measures in

landscapes where problems with alien species have

0

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perc

enta

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f in

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ed c

ells

habitat suitability rank

random sampling

201020302050

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

perc

enta

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ells

habitat suitability rank

fixed order sampling(a) (b)

201020302050

Fig. 4 Percentage of grid cells (30 9 50 geogr. minutes,

*35 km2) infested by Ambrosia artemisiifolia in different

years as a function of the cells’ habitat suitability rank. Ragweed

spread simulations are run under a management design which

assumes that surveillance and eradication are limited to the most

suitable 70 % of the cells (i.e., a minimum habitat suitability

rank of 0.3) and that the selection of cells for annual

management campaigns is performed randomly (a) or following

the order of suitability ranks (b), respectively

How to account for habitat suitability

123

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already become severe (Hulme 2003; Myers and

Bazely 2003), and lacks coordination at a larger scale.

This is ineffective inasmuch as yet small populations

in suitable areas may grow rapidly and start to act as

efficient foci of further spread long before local

population sizes are perceived problematic (Hulme

2003). Efficient control of spread hence requires the

systematic surveillance of areas irrespective of

whether they are known to already harbour popula-

tions of the target species or not. It seems obvious that

designing such a surveillance protocol should take

account of the environmental suitability of particular

areas for the species of interest (Fox et al. 2009) as

spending money searching for a species where it

cannot thrive means wasting resources. Indeed, our

simulations suggest that guiding surveillance by

suitability criteria can generally be effective, but the

precise way of optimally implementing a suitability

based management protocol is not self-evident.

Our expectation was that for a species that still

spreads optimizing effectiveness (for a given budget)

will depend on minimizing search costs without

neglecting marginally suitable cells. When selecting

areas randomly each year an intermediate minimum

suitability rank p proved best for geographically

constraining surveillance and eradication measures.

Too high minimum suitability ranks p are particularly

counter-productive: effectiveness is high during the

first years, because of a low search effort compared to

eradication success, but declines steadily with time as

further control is restricted to re-colonisations and

spread proceeds elsewhere. By contrast, too low

minimum suitability ranks p lessen management

success by twenty five percent at most. As a corollary,

our simulations suggest that under a random sampling

strategy maximizing the area of surveillance is more

advisable than focussing on few particularly suitable

sites (Fig. 2a). Or, put it another way, when budget

constraints require a decision between continuous

control of the most susceptible areas and low-

frequency control of the complete region, the latter

should be preferred as it reduces further spread much

more efficiently. In terms of the long-standing discus-

sion of whether control should focus on core or

satellite populations (Moody and Mack 1988; Hulme

2003; Epanchin-Niell and Hastings 2010) our simu-

lations hence suggest that a restriction to core

populations is inefficient. We concede, however, that

this result may depend on the relatively large and

continuous distribution of suitable habitat in this

particular system (Fig. 1a) and might not hold true

when suitable areas are patchy and dispersed (as e.g.,

for water-bound species in terrestrial contexts).

A rather surprising result of our simulations is that

a simple way of basing surveillance schemes on

suitability criteria is quite effective: defining the

sequence of areas to be surveyed (across several

years) according to suitability ranks. The advantage of

such a fixed-order scheme is that it starts management

where it is most urgent (highest suitability and hence

highest likeliness of being infested and, probably,

highest population growth rate and propagule pro-

duction) and postpones measures in less vulnerable

areas. However, this strategy only becomes fully

effective if those less vulnerable sites will actually be

surveyed at a later stage—the advantage of including

a large part of the whole region into surveillance

measures is even more pronounced with a fixed-order

than with a random sampling scheme. In summary,

the most effective suitability-based surveillance

scheme—among those considered in our simula-

tions—is hence less characterized by a restriction of

controlled sites by some minimum suitability rank

p but by a definition of the temporal control sequence.

In addition, the exact minimum suitability rank p that

maximizes management success is system specific as

it is determined by a trade-off between the rate by

which high-suitability sites become re-colonized after

eradication and the probability that sites of lower

suitability become colonized at all, i.e., the interplay

of a species’ dispersal ability and niche breadth with

the spatial pattern and quality of available habitats. By

contrast, the superiority of a suitability-based fixed-

order site selection scheme per se is supposedly

generic, or at least valid across many different

systems. Fortunately, such ranking is close to what

is often intuitively done in practice and advocated

when the only information available is habitat

suitability (Underwood et al. 2004; Fox et al. 2009;

Giljohann et al. 2011).

Caveats

Our analysis corroborates the expectation that taking

account of habitat suitability will improve the cost-

effectiveness of invasive alien species management

schemes. Species distribution models are tools for

R. Richter et al.

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predicting habitat suitability and hence useful for

guiding the spatial design of such management

protocols. In an applied context, the value of this tool

will, however, depend on how accurately a species

distribution model indicates the suitability of individ-

ual landscapes. In our simulations, we implicitly

assume high accuracy as the species distribution

model we use for selecting the cells to be surveyed

is the same as the one that drives the spread

simulations. In practice, species distribution models

parametrized with available data from either the native

or introduced range (e.g., Kriticos et al. 2003;

Dullinger et al. 2009) will probably measure suitabil-

ity less accurately, either because the species’ distri-

bution has not yet reached an equilibrium with the

environmental conditions in the new range (William-

son et al. 2009), or because of eventual niche shifts

between native and introduced ranges (Broennimann

et al. 2007), or, simply, because important variables

determining suitability have not been accounted for in

the species distribution models (Guisan and Thuiller

2005).

We additionally note that the spread model which

our simulations are based on makes some important

simplifications inasmuch as it does not account for a

natural background mortality rate and also neglects the

process of local population growth within infested

cells. As a consequence, our calculations do not

account for different population sizes and hence

different eradication costs in cells colonized at different

time points. Moreover, we assumed a perfect detection

of ragweed in surveyed cells. We expect that a more

realistic model that incorporates some (e.g., Krug et al.

2010; Regan et al. 2011) of these aspects might affect

the optimum minimum suitability rank p for surveil-

lance restriction quantitatively; it might, for example,

appear less efficient to sample marginally suitable cells

if background mortality rates are higher and population

growth rates lower there than in highly suitable areas,

and the superiority of the fixed-order surveillance

sequence will be less pronounced under imperfect

ragweed detection. However, as it will still hold true

that a cell has highest probability of being occupied if

its abiotic suitability is high and has not been surveyed

recently, we are confident that our conclusion about the

efficiency of a suitability-based fixed-order surveil-

lance sequence is qualitatively robust.

For transferring our results to real-world manage-

ment problems a few additional points should be

considered: as already mentioned above, our distinc-

tion of ‘‘occupied’’ and ‘‘unoccupied’’ cells should be

understood as cells with sizeable and negligible

populations, because accidental transient colonization

events are certainly frequent in any spread process.

Our results are still valid under such a less restrictive

interpretation, as small transient populations can

conceptually be thought of contributing to a low-

level, spatially constant background of seed influx.

Consequently, surveillance and detection of popula-

tions means to determine whether there exists a

sizeable population of the species within the cell,

and eradication corresponds to diminishing these

populations to the negligible level (as opposed to

eliminating every single individual), together with

follow-up visits to these patches to suppress re-

emergence from the seed bank. These are clearly

feasible tasks appropriate to keep infestation density at

low levels and under control.

The cell size on which surveillance and eradication

measures are simulated in this study is quite large

(35 km2) owing to the resolution of the species

distribution data that we use to parametrize our model.

However, the environmental variables that are inputs

to species distribution models are normally known

with much better resolution, and species presence-

absence data might also be derived from sampling plot

data. Real-life implementations of our protocols might

hence use much smaller cells, especially in regions of

pronounced small-scale habitat variation (such as

alpine valleys). Obviously, this will not change our

qualitative conclusions.

Our proposed management protocols are conserva-

tive in that they only use habitat suitability informa-

tion as opposed to incorporating explicit spatial

information about the spread process such as, for

example, preferred surveillance of cells that are

adjacent to locations that have been recognized as

infested in previous years. We did so because it is both

methodically more complex to parametrize spread

models compared to species distribution models, and,

more importantly, the parameters for the latter are

statistically better defined (see Table 1). As we do not

use the spread model for taking decisions in our

management protocols, we are confident that our

conclusions are not affected by these uncertainties. In

the present case of a species that spreads by compar-

atively large distances and that already has established

populations across the whole range, the potential gain

How to account for habitat suitability

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by including spatial information into surveillance

schemes is probably small. In the case of spread

processes with a well-defined invasion front or focus,

however, such information would likely further

improve surveillance protocols.

In this paper we did not attempt to carry out a cost-

benefit analysis as e.g., performed by Mehta et al. (2007)

for various prototype scenarios of invading species. We

did not do so because the costs caused by allergies from

ragweed pollen can presently only roughly be estimated.

An estimation still needs input from aerobiology and

medicine as well as from human population statistics,

work in this field being in progress.

Conclusions

Designing co-ordinated surveillance and eradication

programs of spreading invasive organisms at large

spatial scales will become an increasingly urgent

challenge given that alien species introduction rates

have grown rapidly during the recent decades (Hulme

et al. 2009; Essl et al. 2011a, b). Species distribution

models are now widely used by practitioners and

management institutions with the increasing availabil-

ity of spatially explicit data and appropriate software

packages (e.g., Thuiller et al. 2009). Here, we have

shown that taking account of habitat suitability, as it is

indicated by the predictions of such species distribu-

tion models, can improve the cost-efficiency of

invasive alien species management protocols (assum-

ing accuracy of the used species distribution model).

In particular, it seems rewarding to base spatial

prioritization schemes on suitability ranks of individ-

ual landscapes. A lower minimum suitability rank p (a

suitability threshold) for excluding areas from such

management schemes cannot generally be determined

but our simulations suggest that such threshold should

rather be set too low than too high.

Regarding the case of A. artemisiifolia invasion in

Austria and Bavaria, our spread simulations suggest

that under a no-management scenario the species will

increase its range considerably during the next

decades. We note that this prediction is still conser-

vative as it disregards the likely warming of climatic

conditions which are assumed to additionally favour

the regional range expansion of ragweed (Essl et al.

2009). Given the considerable health costs caused by

ragweed pollen, an immediate and co-ordinated

management response to this species seems advisable.

Our analysis indicates that there still is potential in

slowing, or even reversing the future regional spread

of A. artemisiifolia.

Acknowledgments This work was supported by the Austrian

Academy of Sciences within the Global Change Programme.

We are grateful to R. May, H. Niklfeld, L. Schratt-Ehrendorfer,

and T. Englisch for access to the data of the Floristic Mapping

Projects of Austria and Germany. Valuable unpublished

distribution data have been provided by numerous other

colleagues. We are grateful to two anonymous reviewers for

their constructive comments and in particular to editor Joslin

Moore for her detailed and encouraging suggestions.

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