Use of an individual-based model to forecast the effect of climate change on the dynamics, abundance...

Use of an individual-based model to forecast the effect ofclimate change on the dynamics, abundance andgeographical range of the pest slug Deroceras reticulatumin the UK

J E N N A C . W I L L I S *, D AV I D A . B O H A N *, Y O O N H . C H O I w , K E LV I N F. C O N R A D * and

M I K H A I L A . S E M E N O V *

*Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK, wCentre for Infections, Health Protection Agency,

61 Colindale Avenue, London NW9 5HT, UK

Abstract

Slugs are serious agricultural pests and their activity is strongly driven by ambient

temperature and soil moisture. The strength of this relationship has been shown through

the development of a deterministic model, based upon temperature and soil moisture

conditions alone, which accurately describes the population dynamics and abundance of

Deroceras reticulatum. Because of this strong climatic dependence, slug abundance and

dynamics are likely to be affected by climate change. We used a validated individual-

based model (IbM) of D. reticulatum, to assess the effects of climate change on the

abundance of this species in the UK. Climatic scenarios were based on the UKCIP02

predictions and constructed using the LARS-WG stochastic weather generator. The IbM

of slugs predicted population dynamics at three time slices (2020s, 2050s and 2080s), and

two scenarios of greenhouse gas emissions. The maximum generation number, the

number of population peaks, the number of slug-days in each season, the percentage

of years when the population passes over a threshold for damage and the percentage of

years in which populations go extinct were investigated. Currently, the south-west of the

UK has the best conditions for D. reticulatum to thrive, with the north-east of Scotland

having the most adverse. By 2080 under both low- and high-emissions scenarios, the

north and west of Scotland will have the most favourable conditions for the survival of

this species and the east of the UK and Scotland will have the harshest. By 2080 the

climate in the north-west of Scotland will become more like the current climate in south-

east England, which explains the shift in the pattern of abundance. The north-west of

Scotland will have increased slug damage and south-west England and west-Wales will

have decreased slug damage with some changes becoming evident by 2020.

Keywords: climate change scenarios, slug population dynamics, stochastic modelling, UKCIP, weather

generator

Received 24 October 2005; revised version received 19 December 2005; accepted 20 December 2005

Introduction

Deroceras reticulatum (Genus Deroceras), the grey

field slug, is a major agricultural pest, thriving in the

disturbed environments created in agricultural fields

(Barker, 1991). D. reticulatum is found across the UK and

much of the rest of the world, having been introduced

into North America, Peru, Tasmania, New Zealand and

Central Asia (Yildirim & Kebapci, 2004), as it is a highly

successful invader.

Slugs cause economic damage to crops by eating the

shoots and endosperm of germinating seeds, occasion-

ally causing whole areas of the field to be resown. This

results in the application of d10 million of slug pellets

each year in field crops in England and Wales alone

(Garthwaite & Thomas, 1996). Slugs can also cause

cosmetic damage; the presence of slugs themselves

can devalue the crop. Potatoes and winter wheat are

Correspondence: Mikhail Semenov, tel. 1 44 1582 763 133

(Ext. 2395), fax 1 44 1582 760 981, e-mail:

[email protected].

Global Change Biology (2006) 12, 1643–1657, doi: 10.1111/j.1365-2486.2006.01201.x

r 2006 The AuthorsJournal compilation r 2006 Blackwell Publishing Ltd 1643

thought to be particularly vulnerable to economic losses

due to slugs (Port, 1986), therefore, accurate application

of control measures is essential in these crops. However,

the timing of slug damage is difficult to predict and

control measures are often ineffective, resulting in eco-

nomic loss and damage. Application of slug pellets is

increasing, with 70 times more slug pellets applied in

the 1990s compared with the 1970s (Garthwaite &

Thomas, 1996), possibly indicating an increase in slug

damage.

The most frequently applied pellets are metaldehyde-

based accounting for 55% of pellets applied in UK

arable and horticultural crops, with 40% of pellets being

methiocarb based and the remaining 5% based on

thiocarb (Garthwaite & Thomas, 1996). Slug control

can also be achieved using biological control agents

such as the nematode Phasmarhabditis hermaphrodita

(Schneider) (Ester et al., 2003), although this is not yet

used widely as it is a relatively expensive control

method. Natural predation by mammals, amphibians,

reptiles and even fish (South, 1992; Allen, 2004) has

been documented. Invertebrates including spiders

(Nyffeler & Symondson, 2001), flatworms (Winsor

et al., 2004), Diptera (Barker et al., 2004) and carabid

beetles (Port et al., 2000) are known to predate slugs and

provide some population control. Long-term shifts in

predator populations with changing climate may also

be an important factor in determining future slug

population dynamics and range.

Slugs are ectothermic and have a permeable cuticle.

Correct environmental conditions are essential for their

activity and survival. For example, movement requires

the production of a slime trail that contains a high

proportion of water (South, 1992). Slugs must, therefore,

adapt their behaviour to conserve water and survive

long periods of adverse weather conditions by exploit-

ing cracks in the soil, moving down to warmer, wetter

soils and becoming inactive (South, 1992). The egg stage

can survive for many months if batches remain undis-

turbed, however, even given these behaviours, periods

of frost or drought result in population crashes (Choi

et al., 2006).

The environmental dependence of slugs has been

exploited to produce predictive models of slug popula-

tion dynamics and abundance. A deterministic model

of grey field slug population dynamics has been devel-

oped based upon temperature and rainfall (Choi et al.,

2004). Average daily temperature and total rainfall at a

given site can be used to predict the numbers of grey

field slugs each day. The deterministic model was

developed using initial parameter values from the

literature, which were then calibrated using data taken

from a long-term field trial in Oxfordshire (Christian

et al., 1999). Three and a half years of data from the same

site were used for validation, which explained some

81.2% of the variance in the observed data and captured

the trends in slug numbers and dynamics well across

years. However, this deterministic model was calibrated

at a local, field-level and, therefore, might not be a

suitable tool with which to make UK wide predictions.

For this reason, a more complex individual-based sto-

chastic model was developed incorporating additional

functions describing the time delays between the laying

of egg batches and their hatching time (Choi et al., 2006).

Here, the individual-based model (IbM) of slug po-

pulation dynamics is used to make regional forecasts of

population sizes and dynamics under climate change

scenarios. The initial parameter values for the IbM were

taken from the literature and the calibrated values of the

previous deterministic model. The IbM simulates grey

field slug populations by determining the favourability

of the temperature and rainfall conditions on a parti-

cular day, giving the probability of slugs moving into

cells with food, gaining weight, laying eggs and dying.

The numbers of adults, juveniles, neonates and eggs,

are simulated on each day during the model run. Each

field cell in a virtual arena is also simulated; the damage

caused by slug visits affecting the status of each cell.

The IbM has been validated against long-term field-

sampled datasets from both Letcombe Laboratory in

Oxfordshire (Christian et al., 1999) and Long Ashton

Research Station in Somerset (Prew et al., 1990), show-

ing a good correlation between observed and predicted

slug numbers and demonstrating the IbM’s ability to

predict well at different sites.

The potential effects of long-term climatic change on

the abundance of D. reticulatum in cereal crops have not,

to our knowledge, been demonstrated. However, field

studies on calcareous grasslands indicate that increased

summer rainfall could cause a decrease in the relative

numbers of D. reticulatum (Sternberg, 2000). This effect

was probably due to increased plant growth which

caused a reduction in seedling numbers, which are

the preferred food for this slug. The heating of plots

caused increases in relative slug numbers in February

and January, due to increased winter survival

(Sternberg, 2000).

Modelling studies have shown the effects of climate

change in other species. In butterflies, qualitative pre-

dictions suggest that many populations will increase

with temperature. Of 31 species simulated using pre-

dicted climatic changes, only one had reduced num-

bers, a few remained stable and the rest had increasing

numbers (Roy et al., 2001). This pattern may be true for

many other species of ectothermic animals currently

near the northern edge of their ranges in the UK, areas

of habitat available to these species will increase as UK

climate changes. For other species, near the southern

1644 J . C . W I L L I S et al.

r 2006 The AuthorsJournal compilation r 2006 Blackwell Publishing Ltd, Global Change Biology, 12, 1643–1657

limits of their range in the UK, the opposite may be true

(Thomas et al., 1999). The abundance of nine species of

cereal aphid in southern Britain is likely to decrease in

summer by 2080 (Newman, 2005). The potential for

changes in population dynamics, abundance and range

have also been demonstrated in North Sea cod (Clark

et al., 2003) and in woodland trees (Broadmeadow et al.,

2004).

UK predicted climate change is both favourable and

unfavourable for slugs, making it difficult to form clear

quantitative predictions of future slug distribution

without the use of population models. Predicted in-

creased summer drought will have an adverse effect on

the ability of slug populations to withstand dry periods

(Hulme et al., 2002). Conversely, warmer wetter winters

will favour slugs, as drought and frost are known to

limit populations. A north-west to south-east gradient

of climatic change has been predicted for the UK, with

the south-east having greater changes than the north-

west; geographic changes which may be important

when forming hypotheses about future slug popula-

tions. By considering a number of aspects of slug

population dynamics we aim to address how abun-

dance, dynamics and range will change.

High-resolution climate change scenarios, needed as

an input to the IbM of slugs (Choi et al., 2006), were

developed using a stochastic weather generator

(LARS-WG, Semenov & Barrow, 1997) and based on the

UKCIP02 climate change prediction (Hulme et al., 2002).

Materials and methods

In order to assess the effect of climate change on slug

dynamics and abundance in the UK, we developed high

spatial and temporal resolution climate change scenar-

ios to be used in conjunction with the IbM of slugs.

These scenarios satisfied the following requirements.

The scenarios should:

� be specific down to a site (1 km) or a grid cell (5 km)

with daily temporal resolution;

� be available for any location in the UK, even if

observed daily weather records are missing for the

location;

� include the full set of climate variables required by

the impacts model (i.e. minimum and maximum

temperature, precipitation and radiation);

� be based on the UKCIP02 predictions and include

predicted changes in means and climatic variability;

� contain an adequate number of years to permit risk

analysis.

The methodology is based on the LARS-WG stochastic

weather generator and links predicted changes from a

general climate model (GCM) with the parameters of

the generator (Barrow & Semenov, 1995; Barrow et al.,

1996; Semenov & Barrow, 1997). The algorithm for

spatial interpolation of LARS-WG was developed and

tested in the UK (Semenov & Brooks, 1999). Both these

features are incorporated in the latest release of the

LARS-WG v.4.0 stochastic weather generator, which

was used for this study.1

LARS-WG is based on the series weather generator

described in Racsko et al. (1991) with a detailed descrip-

tion being given in Semenov et al. (1998). LARS-WG

produces synthetic daily time series of maximum and

minimum temperature, precipitation and solar radia-

tion. The weather generator uses observed daily weath-

er for a given site to determine parameters, specifying

probability distributions for weather variables, as well

as correlations between them. The generation procedure

to produce synthetic weather time series is based on

selecting values from the appropriate distributions

using a pseudo-random number generator.

The weather generator distinguishes dry and wet

days depending on whether the precipitation is greater

than zero. Precipitation is modelled using semiempiri-

cal probability distributions for each month, for the

lengths of series of wet and dry days, and for the

amount of precipitation on a wet day. Minimum tem-

perature, maximum temperature and radiation are re-

lated to the amount of cloud cover, and so LARS-WG

uses separate wet and dry day distributions for each of

these variables. The normal distribution is used for the

temperature variables with the mean and standard

deviation varying daily according to finite Fourier

series of order three. Time auto-correlations used for

minimum and maximum temperature are constant

through the year for the particular site and the cross-

correlation of the standardized residuals from the daily

mean is preset for all sites at 0.6. Semiempirical dis-

tributions with equal interval size are used for solar

radiation (for more details see Semenov et al., 1998).

The use of semiempirical distributions gives flexibil-

ity to the generator, allowing it to model a wide variety

of distributions. Analysis of observed data from many

sites around the world showed that the shapes of most

of the variables can vary considerably (Semenov et al.,

1998). Standard distributions were only considered to

be satisfactory for temperature, where the residuals are

modelled by the normal distribution, and the annual

variation in mean and standard deviation by Fourier

series. The weather generator has been tested for

diverse climates in North America, Europe, Asia and

New Zealand and, was able to reproduce most of the

1LARS-WG is available from www.rothamsted.bbsrc.ac.uk/

mas-models/larswg.php.

I B M T O F O R E C A S T T H E E F F E C T O F C L I M AT E C H A N G E 1645


characteristics of the observed data well at each site

(Semenov et al., 1998; Qian et al., 2004; Semenov &

Welham, 2004).

Rather than interpolating the climate variables di-

rectly, the parameters of a weather generator for each

of the observed sites can be interpolated, with the

resulting parameters being used by the weather gen-

erator to produce synthetic daily data for the unob-

served locations. The spatial interpolation procedure

combines global and local interpolation. An initial local

interpolation, based on sites with long daily weather

records (approximately 160 weather stations were used

with � 25 years of records on average), computes for a

selected unobserved site the weighted average of each

of the weather generator parameters from three neigh-

bouring observed sites, which form a triangle contain-

ing the site. We used the Delaunay triangulation

algorithm, which divides the UK area in a set of

adjacent triangles with vertexes at the observed sites.

In the global interpolation, the monthly average pre-

cipitation, temperature and radiation data were spa-

tially interpolated, using a substantially larger number

of sites. Distributions for precipitation, temperature and

radiation of the target site, derived in local interpola-

tion, were adjusted to account for the effects of the

elevation. For this purpose, we used the gridded

monthly climatology provided by the UK Met. Office

(Perry & Hollis, 2004). The interpolation method used

was a two-stage process of multiple regression of the

climate variable (some of them were normalized first)

with geographic factors as the independent variables,

including an elevation along with geographical coordi-

nates, followed by interpolation of the model residuals.

The regression surface and the interpolated residual

surface were added together to get the final gridded

datasets. The monthly values of precipitation, mini-

mum and maximum temperature and sunshine hours

are available for 5 km grid cells for the UK. 4400 sites

were used to interpolate monthly precipitation, 430 sites

for temperature and 290 sites for sunshine hours.

After local interpolation and global adjustment, the

resulting parameter file can be used by LARS-WG to

generate baseline synthetic daily weather data series of

any length for the target site.

UKCIP02 scenarios

The UKCIP02 climate change scenarios are based on a

series of climate modelling experiments completed by

the Hadley Centre, using HadCM3 and HadRM3 cli-

mate models (Hulme et al., 2002). The scenarios, based

on global emissions scenarios published by the Inter-

governmental Panel on Climate Change (Nakicenovic &

Swart, 2000), describe four alternative future climates

for the UK labelled, respectively, low emissions (LO),

medium-low emissions (Med-LO), medium-high emis-

sions (Med-HI) and high emissions (HI) and available

for three time periods 2020, 2050 and 2080.

The HadCM3 climate model is based on physical

principles, describing the transport of mass and energy;

these equations are solved at 30 min intervals for a

number of points over a globe-wide grid. In the

HadCM3 model this grid is 2.51 in latitude by 3.751 in

longitude, corresponding to about 265 km� 300 km

over the UK. The UKCIP02 scenarios are based on

the regional climate model HadRM3, which has a hor-

izontal resolution of 0.441� 0.441 (50 km), and a time step

of 5 min (Fig. 1). HadRM3 takes boundary conditions

from coarser resolution HadCM3 simulations and pro-

vides a higher spatial resolution of the local topography

and more realistic simulations of fine-scale weather

features. For each grid-cell UKCIP02 scenarios provide

changes in monthly mean variables, such as monthly

precipitation, monthly mean minimum and maximum

temperature and monthly mean radiation. These

changes are provided for each time period 2020, 2050

and 2080 and for each emission scenarios LO, Med-LO,

Med-HI and HI.

Fig. 1 The spatial resolution of the HadRM3 climate model

(50� 50 km2 grid cells), highlighted cells were used for simulat-

ing UKCIP02 scenarios in this study. Cells in black were used for

between site comparisons.

1646 J . C . W I L L I S et al.


Construction of climate scenarios using LARS-WG

Daily climate change scenarios were generated by the

LARS-WG stochastic weather generator, based on UK-

CIP02 projections and daily output from the HadCM3

climate model. The construction of a daily climate

change scenario was a two-step procedure. First, for a

selected site in the UK we calculated LARS-WG para-

meters for baseline weather, using interpolation proce-

dure described above. The second step was to derive

changes in mean and variability of climate character-

istics for the site, using monthly UKCIP02 projections

for temperature, precipitation and radiation and daily

precipitation output from HadCM3. The mean changes

in total monthly precipitation, changes in monthly

mean maximum and minimum temperature and

changes in monthly mean radiation were required.

These data were available from the UKCIP02. Changes

in duration of monthly mean dry and wet spells were

calculated using HadRM3 daily precipitation. The

LARS-WG scenario file was used to adjust baseline

parameters, producing new LARS-WG parameters for

the selected site and climate change scenario. These

parameters were then used to generate daily data based

on a particular climate-change scenario.

Modelling slug dynamics

Fifteen, 50 km grid squares were selected to represent

an even distribution of sites across the UK (Fig. 1).

LARS-WG was then used to generate daily climate

scenarios for each of the grid squares. Scenarios were

generated for baseline (1960–1990s), 2020s, 2050s and

2080s time periods for both LO and HI. Baseline cli-

matic conditions are representative of current weather

in the UK and can therefore be used to indicate current

slug populations across the UK enabling us to draw

comparisons. Each scenario contained 70 years of

weather data and was used to simulate grey field slug

population dynamics using the slug IbM. Numbers of

adults, juveniles, neonates and eggs were set to 32, 197,

164 and 600 respectively on 1st March every other year

as slug populations are reset each year as a result of

anthropogenic disturbances such as cultivations. Gen-

eration numbers were also reset to 1. In order to exclude

effect of the starting population values, the population

statistics were calculated for each second year, resulting

in 35 replicates at each site for each scenario.

Metrics and statistical analyses

IbM for slugs is a stochastic simulation model and

produces stochastic output. To compare various simu-

lation runs we have selected and used the following

slug population-dynamic statistics.

Extinction

Extinction was defined as when a population has no

adults, juveniles, neonates or eggs on the last day of

every other year in the simulation run. We calculated

the percentage of replicates in which the population

became extinct for each site. Extinction gave an indica-

tion of the current and future range of grey field slug

populations. Areas with low extinction are likely to

have persisting slug populations, those with high ex-

tinction rates may experience few years with slug

problems as slugs do not survive well at these sites.

Changes in extinction rates demonstrate the potential

for range shifts as they show areas where slugs are more

likely to survive and therefore where populations will

persist.

Generation number

In order to demonstrate changes in the population

dynamics of the grey field slug with climate, we la-

belled individual slugs with a generation number, start-

ing with 1 for slugs used to initialize the simulation run.

The maximum generation number reached at each site,

under each climate scenario, were used for analysis. The

maximum number of generations reached in a year

were then compared between sites and scenarios, using

the Mann–Whitney U-test as the data were not normally

distributed. Previous research has shown that, in slugs,

a larger number of generations are reached in more

favourable years (Hunter & Symonds, 1971). Therefore,

we consider this as an indicator of favourability, de-

scribing how population dynamics in this species might

alter with climate.

Population peaks

Here, we investigated the number of population peaks

in a year as they have been previously linked to the

number of generations in a year and because they reflect

environmental favourability. Greater numbers of popu-

lation peaks indicate whether the local environment is

favourable for slugs to reproduce. A relationship estab-

lished from the literature enables us to define a link

between generations and peaks forming a robust defini-

tion of a peak in terms of a threshold population size

and a duration over which a threshold population size

is exceeded (Hunter & Symonds, 1971).

We defined a population peak by systematically

varying the duration and threshold value of peak, and

comparing this with the maximum number of genera-



tions in a given simulation run showing the most

accurate peak definition. Using this method we defined

a population peak as a period when the average num-

ber of juveniles plus adults exceeded a threshold of five

per m2 for a period of 6 consecutive days.

The number of peaks under baseline conditions was

compared with the number of peaks predicted under

scenarios of climate change using Mann–Whitney U-tests

as the data were not normally distributed.

Slug-days

We calculated the accumulated number of slug-days in

each season by summing the daily totals of adults and

juveniles across days. Therefore, slug-days for any

given scenario are:

slug-days ¼XSk

i¼1

Ai þ Jið Þ;

where Ai and Ji are the numbers of adults and juveniles,

respectively, on day 5 i and Sk is a number of days

in a season k 5 1 . . . 4. Spring was defined as March–

May, summer as June–August, autumn as September–

November and winter as December–February. We

compared the number of slug-days predicted for dif-

ferent scenarios of climatic change with the number

generated for baseline data using a two-tailed t-test, for

each season.

The number of slug-days at each site under each

climate change scenario demonstrates how slug

abundance and more importantly, slug damage may

change with climate. As slug abundance increases, crop

damage is also likely to increase, as does the probability

of populations persisting at a given site. Variations in

slug-days between seasons are also significant with

respect to the timing of crop vulnerability to slug

damage.

Years over a threshold for damage

We analysed the percentage of years in which there is a

daily total of more than 44 adults plus juveniles per m2

in a season. This threshold represents the population

size at which economic damage to wheat crops is

possible given the correct environmental conditions,

from our experience of slugs in the field. Using changes

in the percentage of years with spring, summer, autumn

or winter days over a threshold for damage, we showed

how grey field slug abundance and damage may

change with climate.

We analysed all results in GenStats (Payne et al.,

2005), which then been interpolated and plotted using

MapInfo to visualize changes across the UK. After

analysis of the plotted maps, we chose two sites for

regional comparison and five for comparison between

baseline and 2080 HI scenarios, indicted in Fig. 1. We

interpolated data using the Natural Neighbour algo-

rithm from Vertical Mapper.

Results

Under baseline climatic conditions, the most favourable

conditions for grey field slugs in the UK are the south-

west of England and Wales; this was reflected in all of

the measures of population dynamics, abundance and

range. By 2080, under both HI and LO scenarios, the

north and west of Scotland will have the most favour-

able conditions for this species; the most favourable

areas having shifted northwards. The results also show

that, for some of the measures considered, the varia-

bility in environmental favourability across the UK may

also change. Currently, most measures show that there

is a relatively smooth gradient of change in favourabil-

ity across the country, however, by 2080 both very

favourable areas and very adverse areas may become

increasingly isolated. Generally the results show

broadly the same trends, showing that environmental

favourability for this species is reflected by all the

measures chosen.

Extinctions

When the percentage of replicates in which populations

become extinct under baseline climatic conditions is

mapped, the west of the UK has the lowest extinction

rates (0–10%), with the exception of the central-western

coast of Scotland. Eastern Scotland has high extinction

rates (over 91%; Fig. 2a). By 2020, under both LO and

HI scenarios, extinction rates increase in the east and

in central England from 40% to 60% to at least 64%

(Fig. 2b). By 2050 extinction rates in southern Wales and

south-western England will have increased to at least

38% (Fig. 2c), a trend which becomes more pronounced

by 2080 (Fig. 2d). The western half of Scotland and

northern Wales is predicted to have extinction rates of

under 10% by 2080.

Generations

Under baseline conditions D. reticulatum populations in

the south-west of England and Wales reach 3.9 genera-

tions a year, with the south-west and northern tip of

Scotland also reaching relatively high generation num-

bers. Populations in the east of Scotland reach just 1–1.2

generations (Fig. 3a). Under the 2020s emissions scenar-

ios the maximum generation number reached will

1648 J . C . W I L L I S et al.


decrease throughout most of England and southern

Wales (Fig. 3b). By 2080 the areas of Scotland which

had high generation numbers under baseline conditions

will spread to meet each other, covering the whole of

the western half of Scotland. Areas of high numbers of

generations remain in northern Wales (Fig. 3d).

All of the five sites chosen for statistical comparison

between baseline and 2080 HI scenarios showed sig-

nificant changes in the maximum number of genera-

tions reached (Table 2). The north-west and east of

Scotland showed an increase in the maximum genera-

tion number reached, whereas south-western and cen-

tral England and the west of Wales reached decreasing

maximum generation numbers (Table 1).

Peaks

Under baseline conditions the north of Scotland, wes-

tern England and Wales show the highest number of

population peaks, around 1.9–2.1 peaks yr�1. Eastern

Scotland reached the lowest maximum number of po-

pulation peaks in a year; just 0.14–0.33. Under the 2050

LO scenario, much of the UK will experience less than 1.1

population peaks yr�1, with the western coast having

higher numbers. The south-west of England will have

the highest number of population peaks. By 2080 the

western coast of Scotland and the north-western tip of

Wales will have the highest number of population

peaks, the rest of the UK will have less than 1.3 popula-

10

20

30

40

50

60

70

80

90

100

Baseline 2020 LO

2020 HI

2050 LO 2080 LO

2050 HI 2080 HI

(a)

(c) (e) (g)

(b) (d) (f)

Fig. 2 (a–g) Maps showing UK wide % of years in which populations became extinct under: baseline, 2020 LO, 2020 HI, 2050 LO, 2050

HI, 2080 LO and 2080 HI-emissions scenarios, respectively.



1.2

1.5

1.8

2.1

2.4

2.7

3.0

3.3

3.6

5.0

Baseline 2020 LO

2050 LO 2080 HI

(a) (b)

(c) (d)

Fig. 3 (a–d) Maps showing the predicted maximum generation number reached across the UK of slugs under baseline, 2020 LO, 2050

LO and 2080 HI-emissions scenarios, respectively, in average number of generations in a year.

Table 1 A comparison of site 142, in Scotland, with site 369, in west-Wales under both baseline and 2080 HI-emission scenarios

Measures Generations Peaks

Slug-days

Spring Summer Autumn Winter

Baseline 174.5, o0.001 354.5, o0.001 �2.85, 0.006 �1.92, 0.06 �2.72, 0.01 �2.38, 0.023

(1.5,2.9) (0.9,1.8) (625,2065) (534,1639) (373,2781) (24,751)

2080 HI 167.0, o0.001 357, o0.001 2.20, 0.031 3.00, 0.004 4.96, o0.001 3.08, 0.003

(3.3,2.0) (2.1,1.2) (2349,1277) (1729,544) (2798,240) (482,140)

U-test statistics and its P-value are presented, along with means given in brackets (142 followed by 369), probabilities which are not

significant at the 5% level are in bold font.

1650 J . C . W I L L I S et al.


tion peaks yr�1. The main shift northwards in areas

which reach the highest numbers of population peaks

will occur between the 2050s and 2080s, although

decreases in population peaks in much of eastern and

central England will occur by the 2050s (Fig. 4).

When baseline and 2080 HI scenarios were compared

all five sites showed significant changes in the number

of population peaks per year. The same trends were seen

to those shown when the maximum generation number

reached was considered, with the north-west and east of

Scotland having increased numbers of population

peaks, whereas south-western and central England

and the west of Wales showed decreases (Table 1).

Slug-days

Under spring baseline conditions the west of England

and Wales have the highest numbers of slug-days. In

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.5

Baseline 2050 LO

2080 HI

(a) (b)

(c)

Fig. 4 (a–c) Maps showing the numbers of population peaks predicted across the UK under: baseline, 2050 LO and 2080 HI-emissions

scenarios, respectively, in average number of population peaks in a year.



summer, autumn and winter areas of high slug-days

spread northwards slightly into south-west Scotland

and northern Wales. Slugs-days are highest in autumn

reaching 1373 slugs m�2, in all other seasons they reach

just 176 slugs m�2 (Fig. 5a–d).

Under the 2080 HI scenario western and central

Scotland, and northern Wales will have the highest

number of slug-days in spring (4141 slugs m�2). The

north and east of Scotland will have the least numbers

of slug-days (o8 slugs m�2). In summer, projected areas

of lower numbers of grey field slugs will spread into the

east of the UK. The north-west of Scotland, however,

will have significantly higher numbers of slug-days

(P 5 0.01). In autumn, areas with high slug-days will

contract into the north-west of Scotland and the north-

ern tip of Wales. By winter the areas with high numbers

of slug-days will expand slightly. Areas with high

numbers of slug-days are expected to cover much of

central Scotland and the tip of northern Wales. All other

areas are expected to have small populations of this

species.

When the five separate sites were compared statisti-

cally, the south-west of England and west of Wales

showed the same pattern of change with significantly

fewer slug-days in summer, autumn and winter. Slug-

days in spring did not change. Central England had

significant decreases in slug-days in all other seasons.

Slug-days increased in the north-west of Scotland and

remained the same in eastern Scotland in all seasons

when baseline predictions are compared with 2080 HI

scenario (see Table 2 for means, significance levels and

trends).

Years over a threshold for damage

When the results for the percentage of years which

exceed an economic damage threshold are considered

(a daily total of more than 44 adults plus juveniles m�2,

similar seasonal patterns of change are seen between

autumn and winter under both baseline and 2080 HI

scenarios.

Under baseline conditions spring has a high per-

centage of years over the damage threshold around

south-eastern Wales, and in south-western and north-

ern Scotland. In summer, the areas with a high percen-

tage of years over threshold disappear in northern

Scotland, reappearing again in autumn. A similar pat-

tern is seen in winter with areas with high damage

levels on most of the west coast (Fig. 6a–d).

Under the 2080 HI scenario, in spring the western half

of the UK, will have a large percentage of years over the

threshold for damage from mid-Wales northwards. By

summer the south of Wales and south-western England

are expected to have a low percentage of years over a

damage threshold, with high percentages on the west

coast of north Wales. However, no change in autumn to

Spring

(a)

300 600 900 1200 1500 1900 2300 2700 1000033500

(e)

Summer Summer

(b)

Autumn Winter Autumn Winter

(h) (g)

(f)

(d) Spring

(c)

Fig. 5 (a–h) Maps showing the numbers of slug-days predicted across the UK, (a-d) show spring, summer, autumn and winter under

baseline climatic conditions and (e-h) show spring, summer, autumn and winter under a 2080 HI-emissions scenario, in slugs per m2.

1652 J . C . W I L L I S et al.


Table 2 A comparison between the maximum numbers of generations reached, population peaks and slug-days in each season,

under baseline and 2080 HI-emissions scenarios at five sites

Generations Peaks

Slug-days

Spring Summer Autumn Winter

SW England (411) 398.5, 0.011 462.5, 0.016 0.50, 0.62 2.18, 0.034 2.94, 0.006 2.83, 0.008

(3.06,2.4) (2.08,1.49) (2543,2182) (2059,716) (3186,285) (693,91)

– – ns – – –

Wales (369) 290.0, o0.001 487.0, 0.033 1.93, 0.17 2.05, 0.047 2.92, 0.006 1.96, 0.058

(2.9,2.00) (1.84,1.21) (2065,865) (1639,409) (2781,240) (751,140)

– – ns – – ns

Central England (334) 291.5, o0.001 432, 0.006 0.87, 0.38 1.68, 0.1 0.027 0.089 (295,22)

(2.31,1.43) (1.41,0.59) (1597,959) (943,239) (1035,78) ns

– – ns ns –

NW Scotland (142) 81.0, o0.001 463.5, 0.016 o0.001 0.01 o0.001 o0.001

(1.54,3.29) (0.87,2.08) (625,2349) (534,1729) (373,2798) (24,487)

1 1 1 1 1 1

E Scotland (163) 280.0, o0.001 354.0, o0.001 0.77 0.745 0.262 0.64

(1.0,1.6) (0.19,1.0) (408,505) (313,405) (97,239) (29,46)

1 1 ns ns ns ns

For generations and peaks U-test statistics are given followed by P-values, for slug days t-test values followed by P-values are given.

Means are given in brackets (baseline followed 2080 HI-emissions scenario). Nonsignificant differences at the 5% are underlined. 1 ,

a significant increase from baseline to 2080 HI-emissions scenario, �, a significant decrease and ns, no significant change.

Spring

(a)

Spring

(e) (b)

Autumn AutumnWinter Winter

SummerSummer

(h) (g)

(f)

(c) (d)

10

20

30

40

50

60

70

80

90

100

Fig. 6 (a–h) Maps showing the % of years over a threshold for damage in a year predicted across the UK in: spring, summer, autumn

and winter under baseline and 2080 HI-emissions scenarios, respectively.



winter seasonal pattern is expected between baseline

and 2080 HI scenarios (Fig. 6e–g).

Discussions

By combining climate change predictions, the LARS-

WG stochastic weather generator, and an IbM of slug

population dynamics we have shown that by 2080

under both LO and HI scenarios, the areas of the

UK which are most favourable for populations of

D. reticulatum might be expected to shift northwards.

Current predictions of climate change for the UK

suggest that winters will become warmer and wetter

and summers hotter and drier. Warmer, wetter winters

will favour D. reticulatum populations due to a decrease

in the incidence of frosts. However, the drier summers

will have an adverse effect on grey field slug popula-

tions. These conflicting driving forces make it difficult

to make statements about future grey field slug distri-

butions in the UK. Population models are an ideal tool

for answering questions about future slug distribution,

abundance and range, that are so confounded.

Under current, baseline climatic conditions, areas of

the south-west of England and Wales have the ideal

warm, wet conditions for slug activity, yielding signifi-

cantly more generations, population peaks and slug-

days than elsewhere in the UK. Areas further north are

currently too cold for slugs, with periods of frost that

have been known to cause population crashes. Further

east, the UK becomes too dry to maintain populations,

because slugs require moisture for the production of

slime for movement, and will only lay eggs in soils

above 25% soil moisture (Carrick, 1942).

Areas of north-west Scotland are, therefore, likely to

suffer increased numbers of this species, which may in

turn cause increased crop damage. The south-west and

Wales will have significantly fewer population peaks,

generations and slug-days in summer autumn and

winter and are therefore likely to have decreased slug

problems.

Expectations for D. reticulatum in relation to climatechange

From our simulations, there are clear spatial trends

in the abundance, range and dynamics of D. reticulatum

across the UK with different scenarios of climate

change. Areas of the UK with the most favourable

conditions for D. reticulatum will shift northwards from

the south-west of England and Wales into northern

Wales and western Scotland. The north-west of Scotland

was found to have significantly more slug-days, gen-

erations and population peaks than west Wales by 2080

under a HI scenario, a reversal of the trend seen under

baseline conditions. These results are likely to be a

reflection of the predicted north-west to south-east axis

of climatic change (Hulme et al., 2002), which suggests

that the north-west of Scotland will have less extreme

changes in climate than the south-east of England.

These results are consistent with the prediction that

the north-west of Scotland will have less severe climatic

changes than south-east England (Hulme et al., 2002).

Population metrics considered

Qualitatively, extinctions and the percentage of years

over a threshold for damage follow similar patterns of

spatial change. Generations and peaks also show the

same, significant patterns of change. Qualitatively these

patterns are similar to those predicted for extinctions

and years over a threshold for damage. When slug-days

are considered the situation is more complex, however,

with no significant change in slug-days in eastern

Scotland. This suggests that, while slug population

dynamics might change in this area, yearly abundance

will not alter significantly.

Although all the population metrics considered de-

monstrate visible changes in grey field slug abundance

and/or range and/or dynamics by 2080, the time when

these changes become apparent varies with each metric.

Changes in the rate of extinctions are evident under the

2020s LO scenario, whereas the northward shift of

population peaks is not evident until 2080 under a HI

scenario. This may be a reflection of the varying sensi-

tivity of the different measures to climatic change or the

relative effects of different aspects of climatic change

upon D. reticulatum population biology.

Impact on arable agriculture

The seasonal timing of slug population changes will be

crucial for the observed intensity of damage to field

crops. Slug damage to crops, such as wheat, occurs

mainly at sowing when the endosperm of the seed

may be eaten, in the early stages of growth and after

germination when the tops of new shoots can be eaten

away (South, 1992). We found the predicted number of

slug-days over the season changed with the prevailing

environmental conditions. D. reticulatum currently has

peaks of breeding activity at the main times of arable

crop sowing, in the spring and autumn when climatic

conditions for breeding are optimal (Hunter & Symonds,

1971). This is when populations reach the highest num-

ber of predicted slug-days, under both baseline and 2080

HI scenarios. In winter and summer there are few areas

in which grey field slug populations are predicted to be

sustained at high levels, and in these areas there is likely

to be little drought and few frosted days.

1654 J . C . W I L L I S et al.


At sites with high numbers of slug-days in a parti-

cular season, the slug numbers may either have been

sustained at an intermediate level over the whole

season or have a large peak in numbers during that

season. In sustained populations, slugs still may not

cause crop damage. At sites where slug abundance

peaks over a threshold, however, economic damage

may occur very rapidly. Predictions for the percentage

of years over a threshold for damage currently have a

similar pattern of distribution to that shown for the

predicted number of slug-days under baseline condi-

tions at most sites. Where there are high predicted

numbers of slug-days populations show high peaks in

abundance and consequently a high percentage of years

over a threshold for damage. At some sites in south-

west England and in Wales, however, spatial differences

between these metrics suggest that populations of this

species exist at a steady intermediate level throughout

the year and do not peak over the threshold for damage.

By 2080, slug-days and the percentage of years over a

damage threshold should become more closely related

right across the UK.

An interesting, related finding was that the variation

in environmental favourability for slugs across the

country also changed under all of the climate metrics.

Under baseline conditions there appears to be a smooth

gradient between areas of high and low favourability.

However, as the climate changes areas that are rela-

tively favourable for this species become more isolated

with fewer areas of intermediate favourability. This

means that we expect most regions of the country either

have periods of severe slug damage problems or no

slug damage at all.

Such climate-driven changes in pest-species abun-

dance, range and population dynamics will likely be

superimposed on other climate-driven effects within

arable agriculture. Increases in the thermal growing

seasons will influence both when it is possible to grow

crops in the UK and the varieties and species of crops

grown (Davies et al., 1997; Hulme et al., 2002). Such

changes will alter the severity of slug damage in the

field. For example, although greater numbers of

D. reticulatum are predicted in the western half of

Scotland, few crops are currently grown there and

unless this changes, the predicted slug increases will

not cause significant economic problems.

Future climate may also affect the interactions be-

tween D. reticulatum and predators. The carabid beetle,

Pterostichus melanarius is an important predator that is

thought to be significant in regulating slug populations

(Bohan et al., 2000; Symondson et al., 2002). From a

study of the life cycles of four species of carabids,

Butterfield (1996) suggested that the response of car-

abids to changing climate would be a change in dis-

tribution rather than through physiological adaptation.

Fossil evidence also suggests that the composition of

British carabid fauna has changed in the past in relation

to climate and it is reasonable to assume that it may do

the same in the future (Coope, 1978). If the abundance,

range and dynamics of P. melanarius change signifi-

cantly with respect to slugs, then our predictions of

slug damage in the field would be improved by incor-

porating carabid predation.

Slug populations are also controlled by generalist

predators such as amphibians, birds, badgers and

hedgehogs (Runham & Hunter, 1970). There have been

a number of studies on the impact of climate change

upon migratory birds showing that the timing of mi-

gration may alter and even cease with climatic change

(Crick, 2004). Changes in reproductive output have also

been noted (Crick, 2004). It is as yet unclear how the UK

distribution of bird species will change with climate

making it difficult to draw any strong conclusions about

the knock on effects on slug predation. The situation is

likewise complicated for amphibian distribution. Many

amphibian populations are declining throughout the

world although the cause is unclear with a large num-

ber of explanations being explored to explain these

declines (Blaustein et al., 2004). Five out of seven studies

quoted in Beebee & Griffiths (2005) showed changes in

breeding phenology in relation to climate change. How-

ever, the authors also state that it is uncertain whether

climate change is having an effect on amphibian de-

clines. The effects of climate change upon mammalian

species in the UK are less well studied and little

information is available regarding the likely future

distribution and abundance of hedgehogs or badgers.

Changes in slug predator populations with climate will

have knock on effects for the natural control of slug

populations and future research should be considered

when forecasting range, dynamics and abundance.

Assumptions and uncertainties

The IbM models the case of a single field with uniform

initial conditions and uniform weather over the field.

Emigration is balanced by immigration and effectively

the field is a closed system. For local simulation runs,

this assumption would be unrealistic for predicting

D. reticulatum population dynamics. However, when

considering large-scale regional and temporal changes

in slug dynamics, the importance of local field hetero-

geneity and migration events are less important for

generating comparative population metrics.

The model does not allow for metapopulation ‘rescue

effects’ (Choi et al., 2006) where local heterogeneity in

environmental conditions and dispersal between fields

will allow some populations to persist even under



predicted conditions of a 100% extinction probability.

Long-term climatic conditions that produce high extinc-

tion rates might, though, eventually lead to the eradica-

tion of D. reticulatum in these areas and consequently,

we used qualitative changes in extinction rate to indi-

cate how the range of D. reticulatum will alter with

climate.

Previous studies have linked the number of genera-

tions and peaks, with the number of peaks directly

proportional to the number of generations at a site, in

a given year. The numbers of population peaks indicate

the times of year at which there are high numbers of

slugs and hence how often crops are vulnerable. The

timing of peaks may also change with their number, as

many crops are vulnerable at specific times of the year,

the timing of peaks may be crucial in terms of crop

damage. The number of slug generations has also been

linked to environmental favourability at a given site. We

have found, through simulation, that a period of 6 days

with a minimum of 5 slugs m�2 can be used as an

indicator of a slug population peak. Using this defini-

tion, our results indicate that the predicted number of

population peaks is lower than expected from the

number of generations.

The range in the number of population peaks, across

scenarios and time slices, (1.57) is lower than that for the

number of generations (2.9), indicating that the relation-

ship between peaks and generations, particularly under

climate change, is not as straightforward as Hunter &

Symonds (1971) reported. This warrants further inves-

tigation using IbMs and experimental approaches.

However, both peaks and generations did show the

same qualitative and quantitative trends when shifts

with climate change were considered at five sites across

the country.

D. reticulatum is a highly adaptable species that can

exploit the disturbed habitat of arable fields. The cli-

mate change metrics we present in this paper are

subject to a basic assumption that D. reticulatum popu-

lation rates and responses to temperature and moisture

are similar across the UK and will not change through

time. However, it is likely that changes in local condi-

tions and long-term climate will result in local adapta-

tion selecting for D. reticulatum, with distinct population

rate parameters. Local variations in the fecundity of the

slug Arion lusitanicus have been noted by Briner &

Frank (1998), and varying fecundity with environmen-

tal conditions has also been found in the snail Cepaea

nemoralis L. (Wolda & Kreulen, 1973). If slugs are able to

adapt to adverse local conditions, then the predictions

we have made need to be adjusted. However, because

similar patterns of change were predicted for all mea-

sures considered, we believe it is likely that grey field

slug dynamics and abundance will change as predicted,

potentially affecting the future distribution and inten-

sity of slug damage to crops. Future IbM-based predic-

tions of changes in D. reticulatum abundance, range and

population dynamics would need to take into account

the local adaptation of this species were this to become

apparent.

Acknowledgements

JCW was supported by the BBSRC-CASE fellowship; she thanksDe Sangosse Ltd for funding and Julian Park, University ofReading, for guidance and support. We thank UKCIP for pro-viding access to UKCIP02 scenarios. We thank Stephen Powersfor advice on statistical analysis. Rothamsted Research receivesgrant-aided support from the Biotechnology and BiologicalSciences Research Council of the United Kingdom.

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