Environmental change and the phenology of European aphids

Environmental change and the phenology ofEuropean aphids

R I C H A R D H A R R I N G T O N *, S U Z A N N E J . C L A R K *, S U E J . W E L H A M *,

PA U L J . V E R R I E R *, C O L I N H . D E N H O L M *, M A U R I C E H U L L E w ,

D A M I E N M A U R I C E w , M A R K D . R O U N S E V E L L z and N A D E G E C O C U z,E U R O P E A N U N I O N E X A M I N E C O N S O R T I U M §

*Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UK, wINRA, Unite Mixte de Recherche ‘Biologie des Organismes et des

Populations appliquees a la Protection des Plantes’ (BiO3P), BP 35327, 35653 Le Rheu, Cedex, France, zDepartement de Geographie,

Universite Catholique de Louvain, Place Louis Pasteur 3, 1348-Louvain-la-Neuve, Belgium, §Thematic Network Project EVK2-CT-

1999-20001

Abstract

Aphids, because of their short generation time and low developmental threshold

temperatures, are an insect group expected to respond particularly strongly to environ-

mental changes. Forty years of standardized, daily data on the abundance of flying

aphids have been brought together from countries throughout Europe, through the EU

Thematic Network ‘EXAMINE’. Relationships between phenology, represented by date

of first appearance in a year in a suction trap, of 29 aphid species and environmental data

have been quantified using the residual maximum likelihood (REML) methodology.

These relationships have been used with climate change scenario data to suggest

plausible changes in aphid phenology. In general, the date of first record of aphid

species in suction traps is expected to advance, the rate of advance varying with location

and species, but averaging 8 days over the next 50 years. Strong relationships between

aphid phenology and environmental variables have been found for many species, but

they are notably weaker in species living all year on trees. Canonical variate analysis and

principal coordinate analysis were used to determine ordinations of the 29 species on the

basis of the presence/absence of explanatory variables in the REML models. There was

strong discrimination between species with different life cycle strategies and between

species feeding on herbs and trees, suggesting the possible value of trait-based group-

ings in predicting responses to environmental changes.

Keywords: climate, land use, pests, residual maximum likelihood (REML), spatial correlation model,

suction trap, temperature, traits, variance components

Received 2 December 2004; revised version received 17 March 2005 and accepted 21 March 2005

Introduction

Insects comprise about 80% of known animal species

and occupy every terrestrial habitat. They have pro-

found effects on quality of life and social structure.

Many are devastatingly detrimental (e.g. pests of agri-

culture, horticulture, forestry, wood and stored pro-

ducts; vectors of human and animal disease), many

are benign and many are beneficial (e.g. natural ene-

mies of pests, pollinators, decomposers, food for higher

trophic levels such as birds, those of intrinsic beauty).

All are influenced strongly by their environment and, in

order to anticipate their future status, it is vital to assess

how they are likely to respond to predicted changes.

Much work has been, and is being, done to assess the

impacts of environmental changes on insects, but this

almost always involves one, or a very small number of

closely related, species. Findings for one species cannot

usually be generalized. Unless such generalizations can

be made, the value of such findings will remain paro-

chial and expensive investigations will be required for

every situation in which the impacts of environmental

change require assessment. This study examines the

potential for providing generalizations concerning the

responses to change of aphids. It advances previous

studies by considering a large number of species and by

examining whether certain biological traits influenceCorrespondence: Richard Harrington, fax 1 44 1582 760981,

e-mail: [email protected]

Global Change Biology (2007) 13, 1550–1564, doi: 10.1111/j.1365-2486.2007.01394.x

r 2007 The Authors1550 Journal compilation r 2007 Blackwell Publishing Ltd

the set of environmental variables which correlate with

responses to change, and the strength of the correlation.

There are many reasons why aphids are appropriate

and amenable subjects for examining the impacts of

environmental change and for attempts at generaliza-

tion. Of the approximately 4400 known species, around

250 feed on agricultural and horticultural crops

(Blackman & Eastop, 2000) where they can cause signi-

ficant economic damage through removal of phloem

sap and by other means including, together with tran-

sient species in search of host plants, transmission of

viruses. Many aphid species feed on trees and some of

these aphids are important pests of forestry. Compared

with most other invertebrate groups, aphids generally

have a low developmental temperature threshold

(below which no development occurs), often around

4 1C, and a short generation time (the physiological time

taken from birth to the moment of first giving birth),

often around 120 day degrees above the threshold

temperature, provided that temperatures do not rise

significantly above the developmental optimum, often

around 25 1C (see review in Harrington et al., 1995).

Continuously parthenogenetic aphids may thus achieve

18 generations a year in the United Kingdom (Harrington,

1994) giving the potential for a sensational volume

(Harrington, 1994) or weight (Karley et al., 2004) of

aphids. With a warming of 2 1C an extra five genera-

tions a year might be expected (Yamamura & Kiritani,

1998). Although, of course, a range of abiotic and biotic

constraints prevents aphids from attaining anywhere

near their full population growth potential, it is clear

that climatic conditions, especially temperature, are

immensely influential in determining their dynamics,

and climatic changes are likely to affect strongly their

pest status. Land use, especially through effects on the

phenology, distribution and abundance of host plants,

is also inevitably an important environmental influence

on aphid dynamics.

Another reason that aphids are an interesting group

to study with respect to environmental changes is that

they show variation in a range of traits that might be

useful in helping to predict responses to change. For

example, they vary considerably in their life-cycle traits.

All aphids are parthenogenetic and viviparous (females

giving birth to active young, rather than laying eggs,

without the need to mate) for at least part of the year.

Many respond to the longer night lengths of autumn

with the induction of a sexual phase. These are termed

holocyclic. Some do not respond in this way and are

termed anholocyclic. Some species have some clones,

which are holocyclic and some which are anholocyclic.

Within a species, the proportion of individuals which is

holocyclic tends to be greater in colder regions, as the

eggs resulting from sexual reproduction are very much

more cold-hardy than the active, viviparous forms

which persist year round in anholocyclic clones. Some

holocyclic aphid species produce their eggs on entirely

different host plant taxa from those on which most of

the parthenogenetic forms are produced. This is known

as host-alternation, or heteroecy. Usually, the partheno-

genetic generations are produced on herbaceous host

plants, including crops, while the eggs are laid on

woody species. Others produce eggs on the same host

plant taxa as those on which they produce their parthe-

nogenetic generations, and these host plants may be

herbaceous or woody. This is known as monoecy (or

autoecy by some authors).

A third reason for the study of aphids in relation to

environmental change is the availability of daily, long-

term, spatially extensive, standardized data on the

abundance of winged aphids throughout Europe

(Harrington et al., 2004). This paper utilizes these data

to model statistically the phenology (in terms of first

flight record) of several aphid species in relation to a

range of climatic and land-use variables. The strength of

the models (in terms of variance accounted for), and the

nature of the variables necessary to create the strongest

models, are considered in relation to the life-cycle traits

described above, and the potential for generalizing

assessment of responses to change is discussed. Cli-

matic data generated by a change scenario model are

substituted in the models to provide a tentative indica-

tion of possible changes that might be expected in aphid

phenology.

Materials and methods

Data sources

Aphid data. All aphid data used come from the net-

work of suction traps (Fig. 1) co-ordinated by the

European Union-funded thematic network EXAMINE

(Harrington et al., 2004). The traps vary very little from

a standardized design which draws in air at a rate of

approximately 0.75 m3 s�1 through an aperture 12.2 m

above ground level (Macaulay et al., 1988). In most cases,

they are emptied daily during the aphid flight period,

but the number of species identified varies between

countries. At most of the sites, at least 29 species (Table

1), most of which are important pests, are identified if

present, and these form the core data set analysed here.

Table 1 also describes the life-cycle characteristics of

these species. Data from 91 traps are analysed (Fig. 1),

ranging in duration of operation from 1 to 36 years and

providing a total of 1376 trap–years of data.

The aphid variable studied was the Julian date of

first appearance in the trap samples each year. This date

is dependent on both phenology and on abundance.

I N S E C T S A N D E N V I R O N M E N T A L C H A N G E 1551

r 2007 The AuthorsJournal compilation r 2007 Blackwell Publishing Ltd, Global Change Biology, 13, 1550–1564

Advanced phenology in a given year will tend to lead to

earlier first flight records, but abundance also plays a role

because the more aphids that are flying, the greater the

probability that one will suffer the misfortune of passing

over the trap aperture.

For species–site–years with a zero count (sometimes

because the trap was outside the species range (i.e. the

species has never been caught there); sometimes because,

although the species has been recorded at the site, it does

not occur every year; and sometimes because, although

the site is within the species range, it is not a species that

is identified by the team operating that site), no first

flight date could be recorded and the data were omitted

from the analyses. This biases the results a little, as

conditions that result in no aphids flying at all are not

considered. Data were also eliminated from the analysis

if, for any reason, the trap was not operating at a time

when the first record of a particular species might be

expected. Of the 39 904 species–trap–years combinations,

16 581 (42%) were omitted from the analyses for one of

these reasons.

Geographic data (Table 2). Data for longitude (X), latitude

(Y) and altitude (Z) were included as possible

explanatory variables. Because previous work (R.

Harrington and S. J. Clark, unpublished) involving

data from the UK had shown a quadratic relationship

between the first record of aphids in suction traps and

Shaded countries are EU members

Traps still operated

Traps no longer operated

Fig. 1 Suction trap sites.

1552 R . H A R R I N G T O N et al.


longitude and latitude, X2, Y2 and XY terms were also

included. Values of latitude were centred to have

approximately zero mean by subtracting a constant

(50) before computing the quadratic terms in order to

avoid mathematical problems associated with large

data values.

Climatic data (Table 2). Climatic data were extracted from

the EU ATEAM project’s 100 � 100 gridded database

(Mitchell et al., 2004). Data were available up to the

year 2000 and projections to 2100 were provided, based

on a range of greenhouse gas emissions scenarios of

which only one (Hadley Centre A1FI, see ‘Climate

change scenario analysis’) is considered here. Monthly

values for precipitation and mean temperature were

used.

Because a given calendar month represents a

different phase of the aphid phenological cycle in

different locations, the temperature data were

manipulated in order to derive a more useful

explanatory variable that corrects for this. This

comprised an estimate of the mean temperature of the

period of the year which, on average between 1960 and

1999, was the coldest 30 days (temperature for this

period is termed C30) and the mean temperature of

the 60-day period (P60) subsequent to the cold period.

These periods were chosen after preliminary work, not

reported, which tested the temperatures of the coldest

30-, 60- and 90-day periods alone and with each of the

following 30, 60 and 90 days. The coldest 30 days and

the following 60 days were consistently most strongly

correlated with phenology. The methodology avoids

Table 1 Core aphid species

Species English common name Life cycle* Nw Sz

Acyrthosiphon pisum (Harris) Pea aphid AH, mon, herb 1094 75

Aphis craccivora gp. Koch Black legume aphid AA 234 38

Aphis fabae gp. Scopoli Black bean aphid HH, het 1111 77

Aphis gossypii Glover Melon aphid; Cotton aphid AA 51 17

Aphis pomi De Geer Green apple aphid HH, mon, tree 583 44

Aphis spiraecola Patch Spiraea aphid AH, het 88 20

Aulacorthum solani (Kaltenbach) Glasshouse – potato aphid AH, het 913 66

Brachycaudus helichrysi (Kaltenbach) Leaf-curling plum aphid AH, het 1058 77

Brevicoryne brassicae (L.) Cabbage aphid AH, mon, herb 984 74

Cavariella aegopodii (Scopoli) Willow – carrot aphid AH, het 937 64

Drepanosiphum platanoidis Schrank Sycamore aphid HH, mon, tree 670 44

Elatobium abietinum (Walker) Green spruce aphid AH, mon, tree 814 53

Hyalopterus pruni (Geoffroy) Mealy plum aphid HH, het 1062 74

Hyperomyzus lactucae (L.) Blackcurrant – sowthistle aphid HH, het 980 64

Macrosiphum euphorbiae (Thomas) Potato aphid AH, het 1020 77

Metopolophium dirhodum (Walker) Rose – grain aphid AH, het 1086 76

Myzocallis castanicola Baker Chestnut aphid HH, mon, tree 679 53

Myzus ascalonicus Doncaster Shallot aphid AA 929 66

Myzus persicae (Sulzer) Peach – potato aphid AH, het 1102 77

Nasonovia ribisnigri (Mosley) Currant – lettuce aphid HH, het 909 68

Phorodon humuli (Schrank) Damson – hop aphid HH, het 1011 76

Rhopalosiphum insertum (Walker) Apple – grass aphid HH, het 949 65

Rhopalosiphum maidis (Fitch) Corn leaf aphid AA 899 60

Rhopalosiphum padi (L.) Bird cherry – oat aphid AH, het 1145 81

Sitobion avenae (Fabricius) Grain aphid AH, mon, herb 1105 76

Sitobion fragariae (Walker) Blackberry – cereal aphid HH, het 909 62

Therioaphis trifolii (Monell) Yellow clover aphid AH, mon, herb 189 32

Toxoptera aurantii (Boyer de Fonsc.) Black citrus aphid AA 56 16

Tuberculatus annulatus (Hartig) Oak aphid HH, mon, tree 756 48

*HH, largely holocyclic throughout European range; AA, largely anholocyclic throughout European range; AH, may be anholocyclic

or holocyclic; het, where holocyclic it is host-alternating (heteroecious); mon, where holocyclic it does not host-alternate

(monoecious); tree, where monoecious it lives on a woody host; herb, where monoecious it lives on a herbaceous/grass host.wN, number of site-years data.zS, number of sites analysed for each species.



phenological bias and considers the harshest period of

winter, which is most likely directly to deplete the

aphid population, and the subsequent period during

which development to flight would be occurring. To do

this, the temperature for each month, averaged over the

40 years 1960–1999, was taken for each site. These

monthly means were plotted with July as the first

month and June as the last, so that the low

temperature periods were in the middle rather than at

the two ends of the 12-month sequences. The mean

monthly temperature ordinates were interpolated using

a natural cubic spline to provide a smoothed mean

temperature curve for each site, and a minimization

algorithm was used that calculated the 30-day-period

which was, on average, the coldest over the 12 month

period for each site. It was then necessary to estimate,

from monthly values available in the ATEAM climatic

data set, the mean temperature of this period for each

site in each year. This was done by assuming the

temperature to be the same each day of a given

month, apportioning the temperatures of the 30-day-

period pro-rata between those of the months in which

they fell, and calculating a mean value for the

30-day-period. The reliability of this methodology was

checked by regressing these interpolated values on real

daily values available for Rothamsted, UK. Seventy-six

per cent of the variance was accounted for. When the

Rothamsted monthly means for January over the

40 years were plotted against the ATEAM monthly

means for January, 97% of the variance was accounted

for, showing the ATEAM gridded data to be in close

agreement with the Rothamsted synoptic data. There

was thus confidence in the suitability of the data and

the interpolations.

Such a modification was not possible for the rainfall

data because of the difficulty in interpolating monthly

values to provide daily values. Therefore, all rainfall

data used were for calendar months. Data for October–

December of the previous year (OCTPRN, NOVPRN

and DECPRN) and January–May of the year of the

aphid data (JANRN, FEBRN, MARRN, APRRN and

MAYRN) were used.

Table 2 Geographic, climatic and land use variables

Number Geographic Source: EXAMINE database, Harrington et al. (2004)

1 X Longitude

2 Y Latitude

3 Z Altitude

4 X2 Latitude2

5 Y2 Longitude2

6 XY Latitude� longitude

Climatic Source: ATEAM project, Mitchell et al. (2004)

7 OCTPRN Mean rainfall during the previous October

8 NOVPRN Mean rainfall during the previous November

9 DECPRN Mean rainfall during the previous December

10 JANRN Mean rainfall during January

11 FEBRN Mean rainfall during February

12 MARRN Mean rainfall during March

13 APRRN Mean rainfall during April

14 MAYRN Mean rainfall during May

15 C30 Mean temperature of the mean coldest 30 days

16 P60 Mean temperature of the 60 days immediately after the mean coldest 30

Land use Source: PELCOM Land Cover, Mucher et al. (2000)

17 CONFOR Coniferous forest (area in ha in a circle of R 5 75 km)

18 DECFOR Deciduous forest (area in ha in a circle of R 5 75 km)

19 MIXFOR Mixed forest (area in ha in a circle of R 5 75 km)

20 GRASS Grassland (area in ha in a circle of R 5 75 km)

21 WATERI Inland waters (area in ha in a circle of R 5 75 km)

22 URBAN Urban areas (area in ha in a circle of R 5 75 km)

23 ARABLE Arable land (area in ha in a circle of R 5 75 km)

24 SEA Sea (area in ha in a circle of R 5 75 km)



Land-use data (Table 2). No suitable time series data were

available for land use over the whole of Europe. The EU

PELCOM project’s land cover data (Mucher et al., 2000),

a geographical map of 14 land-use classes at 1 km

resolution, obtained from earth observation satellite

images in 1997, were used to allocate land use within a

circle of radius 75 km centred on each trap to eight

classes: coniferous forests (CONFOR), deciduous forests

(DECFOR), mixed forests (MIXFOR), grassland (GRASS),

inland waters (WATERI), urban areas (URBAN), arable

land (ARABLE) and sea (SEA). Other land-use classes

were available, but use of them all would lead to

problems of identifiability as they would sum to near

100% for each trap. Our strategy was to exclude land-

use variables, which could be predicted by latitude (e.g.

occurred only in the south), that might represent a very

heterogeneous class, or where a few nonzero points

could be very influential (i.e. values were zero at most

sites). In each of these cases, interpretation of any

relationship between land use and aphid flight could

be difficult or unreliable.

Data analysis

Relationships between aphid flight data and environmental

data. The relationships between aphid flight data and

explanatory variables were explored using linear mixed

models with random terms to account for the spatial

arrangement of the traps and for the correlation in the

data due to repeated measurements at each trap. The

models were fitted using the method of residual

maximum likelihood (REML) (Patterson & Thompson,

1971). For a simple error model (i.e. independent,

identically distributed normal errors), REML estim-

ation is equivalent to multiple linear regression. For

models with several sources of error (random terms),

REML provides estimates of fixed effects with esti-

mates of variance parameters that are less biased than

maximum likelihood estimates. In general, mixed

models use random terms to account for all sources of

variation and correlation in the data. The fitted model

gives insight into the structure of the data, produces an

appropriate variance model for testing fixed effects, and

generates realistic standard errors (SEs). For example,

the geographic and land-use variables take a constant

value across years at each trap. Using random effects for

traps means that this pseudoreplication is accounted

for. Ignoring this structure could mean that incorrect SE

and degrees of freedom would be used to assess these

variables.

Two sets of models were produced for each of the 29

aphid species. Set 1 used all geographic, climatic and

land-use variables (see Table 2). However, these models

were not suitable for climate change scenario analyses

because much of the climate variability is accounted for

by location which is, of course, stable over time. Set 2

used climate and land-use variables only, and these

models were used for climate change scenario analyses.

In each case, the initial fixed model included linear

terms for all explanatory variables, which were

standardized to have zero mean and unit variance.

The random model included independent random

effects for traps and years, plus a correlated residual

error term structured to allow spatial correlation across

observations within the same year, but independence

across years. The spatial correlation model was an

exponential power model, with correlation for sites A

and B at co-ordinates (xA, yA) and (xB, yB), respectively,

defined as either jD (where D2 5 (xA�xB)2 1 (yA�yB)2) or

jxDxjy

Dy (where Dx 5 |xA�xB|, and Dy 5 |yA�yB|) with

0ojo1 so that correlation decreased with distance.

The analysis was done using GENSTAT 7th edition

(Payne et al., 2003) and the same procedure was used

for all aphid species. First, an appropriate variance model

was found from a series of REML analyses using the

initial (full) fixed model and progressively simplifying

the random model by trying different correlation models,

or setting correlation parameters and/or variance

components to zero. Competing variance models were

compared using the Akaike information criterion (AIC)

(Verbeke & Molenberghs, 2000, section 6.4) to identify the

simplest model that adequately described the variance

patterns. The established variance model was then used

as a fixed weight matrix in a backwards regression

procedure used to reduce the set of explanatory

variables. The final set of explanatory variables and the

variance model were then refitted by REML. Smooth-

ing splines were used to check for nonlinearity in the

response to each explanatory variable, and plots of

residuals and fitted values were used to check for

systematic bias in the models. The final model was

constrained always to include longitude (X) and

latitude (Y), in order to avoid models with quadratic

terms present but no linear terms. Calculation of Wald

statistics showed that X could have been legitimately

dropped (i.e. it did not contribute significantly to

variance accounted for) only in the case of Tuberculatus

annulatus (P 5 0.921), and Y could have been legitimately

dropped only in the case of Myzus ascalonicus (P 5 0.339)

and Therioaphis trifolii (P 5 0.689).

The final model provided a subset of explanatory

variables found to be related to the aphid flight data,

with coefficients and SEs that could be used for scenario

assessment. The percentage of variance accounted for

by the model was calculated as 100� (1�VF/VN),

where VF, VN are the estimated variance of an

observation in the full and null fixed models,

respectively, calculated from the sum of the estimated



trap and year variance components plus residual error

variance. The estimated variance components in the

final model indicate the amount of variation un-

accounted for by explanatory variables. The presence

of spatial correlation indicates a smooth spatial trend

across sites within years, which may represent the

effect of spatially dependent explanatory variables not

present in the model.

Relationships between aphid traits and variables selected in

the REML models. Canonical variate analyses (CVA;

Krzanowski, 1988) were used to analyse the binary

data set representing presence/absence of explanatory

variables in the final models for each species. Separate

ordinations were obtained based on discrimination

between the three life-cycle types and between the

three host use groups. For a CVA with only three

groups, all the variability is represented in two di-

mensions. A principal coordinate analysis (PCO) was

also done to corroborate the CVA groupings. Variables

X and Y were excluded from the PCO as these occurred

for all species, and similarities between species were

computed using the Jaccard coefficient (Digby &

Kempton, 1987).

Climate change scenario analysis. Temperature (C30 and

P60 derived values) and precipitation data generated by

the Hadley Centre A1FI climate change scenario

(Nakicenovic et al., 2000) for each of 15 trap sites

(the sites with the longest data run from each of 15

countries) were substituted in the second set of models

(above). A1FI is a fairly extreme scenario based on a

future world with rapid economic growth, low

population growth and the rapid introduction of new

and more efficient technologies, with energy needs

supplied largely by fossil fuels. It represents a ‘worse-

case’ situation with respect to expected temperature

rises, with an average global increase of 4 1C expected

by the end of the 21st century. The models were run

with these scenario data for each site-species combi-

nation for the years 1965–2050. Expected values for

dates of first aphid records from 1965 to 2000 using

the A1FI scenario model could thus be compared with

observed data and the expected values for the following

50 years displayed. Expected values were regressed on

year to examine temporal trends in date of first aphid

record under the A1FI scenario.

Results

Relationships between aphid and environmental data

The geographic, climatic and land-use variables se-

lected by the REML analysis to explain date of first

record, varied with species (Table 3). However, some

clear overall patterns emerged (Table 4). Seventeen

species of aphid appeared later with increasing altitude

and none earlier. Interpretation of first record in relation

to latitude and longitude is complicated where quad-

ratic terms are present. A surface of fitted values in

terms of only the location variables present in the final

model (Table 3) was computed for each species. Plotting

values of the first derivative of these surfaces with

respect to latitude (not shown) revealed that in 16

species, first flight record was always later with increas-

ing latitude. In only one species, first flight record was

always earlier with increasing latitude. In the remaining

12 species, first flight record was earlier with increasing

latitude in some areas and later in others. Plotting values

of the first derivative of the fitted surfaces with respect to

longitude (not shown) revealed that in five species, first

flight record was always later with increasing longitude.

In three species, first flight record was always earlier

with increasing longitude. In the remaining 21 species,

first flight record was earlier with increasing longitude in

some areas and later in others. Thus it can be said that

there is a strong trend towards later first flight records

with higher altitude and higher latitude, but that the

relationship with longitude is far more variable.

Among the climatic variables, higher rainfall was

associated with later flight 30 times and earlier flight

only four times. Higher temperature was associated

with earlier flight 34 times and never with later

flight. Among the land-use variables more urban land

was associated with earlier flight in 11 species and never

with a later flight. More sea was more often associated

with later flight (14 species) than with earlier flight (four

species). More arable land was associated with later

flight more often (11 species) than earlier flight (three

species). Other land use categories were more evenly

balanced in their association with first flight record.

On average over the 29 species, 44% of variance in

date of first aphid record was accounted for by the

explanatory variables picked by the REML models

(Table 5). The maximum variance accounted for

was 62% (Acyrthosiphon pisum) and the minimum 4%

(Elatobium abietinum). There was little difference bet-

ween those species which are entirely anholocyclic

within their European range (AA, 48% variance ac-

counted for), the entirely holocyclic species (HH, 37%)

and those with both life-cycle types (AH, 47%). Less

variance was accounted for in the case of species which

live year round on trees (AH mon tree 1 HH mon tree,

25% variance accounted for) than for those that live year

round on herbs (AA 1 AH mon herb, 51%) or alternate

between the two (AH het 1 HH het, 45%). When

discriminating between groups with differing life-cycle

types, or host use (Tables 1 and 5), on the basis of



presence/absence of explanatory variables in the REML

models (Table 3), the first CVA axis accounted for 65%, or

70%, of the variability between species, respectively. The

first axis clearly separated the species living only on trees

from the other two groups (Fig. 2a), and there was clear

discrimination between the life-cycle type groups (Fig.

2b). However, the interpretation of the CVA axes was not

straightforward, apparently because of heterogeneity

within the groups, and possibly exacerbated by the small

sample size, and so further details are not presented

here. Instead, the PCO analysis was done to corroborate

the differences seen in the CVA analyses. While the first

three PCO axes (Fig. 2c) only account for 30% of the

variability, this further analysis generally supports the

differences found using CVA and illustrates the reason-

able homogeneity within groups, especially for species

living only on trees (with the exception of A. pomi) and

only on herbs (with the exception of B. brassicae).

Climate change scenario analyses

With 29 aphid species and 15 sites under consideration,

it is not possible to show plots of expected changes in

Table 3 Models of date of first record

Species Model (variable and sign) %var.

Acyrthosiphon pisum 1 ARABLE, 1 CONFOR, 1 DECFOR, 1 GRASS, 1 SEA, 1 WATERI,

1 Z, �X, 1 X2, �XY, 1 Y, �C30, �P60, 1 DECPRN, 1 MARRN, 1 MAYRN

62

Aphis craccivora 1 ARABLE, 1 DECFOR, 1 SEA, �X, 1 Y, �P60, 1 FEBRN 60

Aphis fabae 1 ARABLE, 1 SEA, �URBAN, 1 Z, �X, 1 X2, �XY, 1 Y, �P60 46

Aphis gossypii 1 CONFOR, 1 DECFOR, 1 GRASS, �MIXFOR, 1 SEA, 1 X, 1 Y, 1 OCTPRN, 1 FEBRN 46

Aphis pomi �MIXFOR, �URBAN, 1 Z, �X, �Y, 1 Y2, �C30, �P60, 1 MARRN 33

Aphis spiraecola �X, �XY, 1 Y, �P60 40

Aulacorthum solani �CONFOR, �DECFOR, 1 Z, �X, �XY, 1 Y, �C30, �P60, 1 MARRN 47

Brachycaudus

helichrysi

1 ARABLE, 1 CONFOR, 1 DECFOR, 1 GRASS, 1 MIXFOR, 1 SEA, 1 WATERI, 1 X, �XY, 1 Y,

1 Y2, �P60, 1 DECPRN, 1 MAYRN

57

Brevicroyne brassicae 1 SEA, 1 Z, 1 X, �XY, 1 Y, �C30, 1 MAYRN 44

Cavariella aegopodii 1 ARABLE, 1 Z, �X, 1 X2, 1 Y, �C30, �P60, 1 NOVPRN, 1 DECPRN 45

Drepanosiphum

platanoidis

�DECFOR, 1 MIXFOR, 1 Z, �X, 1 X2, 1 XY, �Y, 1 Y2 36

Elatobium abietinum 1 X, �Y, 1 Y2 4

Hyalopterus pruni 1 SEA, 1 Z, �X, 1 X2, 1 XY, 1 Y, �P60, 1 APRRN 52

Hyperomyzus lactucae 1 ARABLE, 1 DECFOR, 1 GRASS, 1 SEA, �X, 1 Y, 1 Y2, �C30, �P60, �OCTPRN, 1 FEBRN,

1 MARRN

42

Macrosiphum

euphorbiae

�MIXFOR, �URBAN, 1 Z, 1 X, 1 Y, �Y2, �C30, �P60 56

Metopolophium

dirhodum

�MIXFOR, �URBAN, 1 Z, �X, �XY, 1 Y, �C30, �P60 52

Myzocallis castanicola 1 ARABLE, 1 DECFOR, 1 SEA, �X, �XY, 1 Y 39

Myzus ascalonicus �ARABLE, �CONFOR, �DECFOR, �GRASS, �MIXFOR, �SEA, �URBAN, 1 X, �Y, �C30, �P60,

1 FEBRN, 1 JANRN

48

Myzus persicae 1 ARABLE, 1 SEA, �URBAN, 1 Z, 1 X, �XY, 1 Y, �Y2, �C30, 1 NOVPRN, 1 DECPRN, 1 FEBRN 54

Nasonovia ribisnigri �ARABLE, �CONFOR, �GRASS, �MIXFOR, �SEA, �URBAN, 1 Z, �X, 1 X2, 1 Y, �Y2, �C30,

1 NOVPRN

33

Phorodon humuli �CONFOR, �GRASS, �URBAN, 1 WATERI, �X, 1 X2, 1 Y, �P60, 1 MAYRN 44

Rhopalosiphum

insertum

�SEA, �URBAN, �WATERI, 1 X, �XY, 1 Y, �P60, �APRRN 34

Rhopalosiphum

maidis

1 ARABLE, 1 CONFOR, 1 DECFOR, 1 GRASS, �MIXFOR, 1 SEA, �WATERI, 1 Z, �X, 1 X2,

1 Y, �P60

42

Rhopalosiphum padi 1 ARABLE, 1 DECFOR, 1 GRASS, 1 SEA, 1 Z, 1 X, �XY, 1 Y, �P60, 1 MARRN 39

Sitobion avenae 1 ARABLE, 1 DECFOR, 1 GRASS, 1 SEA, 1 WATERI, 1 Z, 1 X, �XY, 1 Y,

�C30, �P60, �OCTPRN, 1 MARRN

57

Sitobion fragariae 1 DECFOR, �MIXFOR, �URBAN, 1 Z, 1 X, �X2, 1 Y, 1 Y2, �C30, �P60, 1 FEBRN 40

Therioaphis trifolii �CONFOR, 1 MIXFOR, �X, 1 X2, 1 Y, �P60, 1 OCTPRN, 1 FEBRN 59

Toxoptera aurantii �ARABLE, �CONFOR, �GRASS, �SEA, �URBAN, 1 WATERI, 1 X, �XY, �Y, �Y2, �P60,

1 DECPRN, 1 NOVPRN, �MAYRN

44

Tuberculatus annulatus 1 SEA, 1 Z, 1 X, 1 Y, 1 Y2 13



date of first flight for all 435 combinations, but they

can be seen on the EXAMINE project web site at http://

www.rothamsted.bbsrc.ac.uk/examine/FinalReport

310304.zip. As an example, Fig. 3 shows, for Myzus

persicae at Rothamsted, the observed dates of first flight

from 1965 to 2000 and the projected values of these from

1965 to 2050 on the basis of climatic changes suggested

by the HADCM3 A1FI model (Nakicenovic et al., 2000).

The predicted values of first flight generally follow the

direction of the observed values but the magnitude of

the more extreme observed values is generally not

modeled well. The trend line shows that the expected

rate of advance of date of first flight record with time

under the scenario is 1 day every 4 years. For all species,

except the two (Drepanosiphum platanoidis and Elatobium

abietinum) for which temperature does not feature in the

first flight model, the scenario analysis shows a trend

towards earlier flight records at all sites. Across all sites,

the mean rate of advance is greatest (approximately

1 day per 3 years) for T. aurantii. Across all species, the

mean rate of advance varies from 1 day per 22 years at

the German site to 1 day per 7 years at the UK site. The

mean across all sites and species is 1 day per 6.25 years

(i.e. 8 days over the next 50 years).

Discussion

Relationships between aphid and environmental data

As with all multivariate statistical techniques, relation-

ships found between the independent and explanatory

variables do not prove cause and effect. Also, with

correlations between some pairs of explanatory vari-

ables (Table 6), it is likely that some variables selected

through use of the REML procedure are masking others

which, on their own, would show a significant correla-

tion with the date of first aphid record. For example,

latitude and temperature are strongly correlated,

although the lack of a significant correlation between

altitude and temperature is surprising and may reflect

the fact that most traps are at low altitude. No signifi-

cant correlations are evident between land-use and

climatic variables, which is perhaps a little surprising.

The land-use categories may be too broad to show this:

although different crop types are sure to be associated

with particular climatic conditions, the land-use class

‘ARABLE’ covers many crops. After a given variable

Table 4 Association of higher values of explanatory variables

with first aphid record in REML models

Variable

Number of associations with

Earlier flight Later flight

X 16 13

Y 5 24

Z 0 17

X2 1 9

Y2 4 7

XY 13 2

OCTPRN 2 2

NOVPRN 0 4

DECPRN 0 5

JANRN 0 1

FEBRN 0 7

MARRN 0 6

APRRN 1 1

MAYRN 1 4

C30 13 0

P60 21 0

CONFOR 6 4

DECFOR 3 10

MIXFOR 8 3

GRASS 4 7

WATERI 2 5

URBAN 11 0

ARABLE 3 11

SEA 4 14

Table 5 % Variance accounted for in REML models (Table 3)

in relation to life-cycle characteristics and host plant usage

Life-cycle type Number of species Mean % variance

AA 5 48

AH mon herb 4 56

AH mon tree 1 4

AH het 8 49

HH mon herb 0 –

HH mon tree 4 30

HH het 7 42

AA 5 48

AH mon herb 1 AH

mon tree 1 AH het

13 47

HH mon herb 1 HH

mon tree 1 HH het

11 37

only on herbs (AA 1 AH

mon herb)

9 51

only on trees (AH mon

tree 1 HH mon tree)

5 25

alternate (AH het 1

HH het)

15 45

all species 29 44

The first grouping separates species according to each of: life

cycle type (AA, anholocyclic, HH, holocyclic or AH, mixed);

whether there is host alternation (het) or not (mon) and, if not,

whether the host plant is a herb or a tree. The second separates

species only according to life-cycle type and the third only

according to host plant usage. Finally, the mean for all species

is shown.



has been selected, only those that add significantly to

the variance explained by the model are included.

Nonetheless, in general, the variables selected and the

signs of their coefficients are as intuitively expected.

Earlier first flight records would be expected with

warmer winters (C30), which tend to increase survival

and rates of development of those aphids over-winter-

ing in the active stages. Warmer springs (P60) would be

expected to increase development rates of the fundatrix

and fundatrigenia stages, which span the period from

egg hatch to flight, and hence lead to earlier flights.

Because of the negative correlation between latitude

and temperature (lower latitude, higher temperature),

higher latitudes would be expected to be associated

with later first flight records, and this is generally the

case. In all 17 species for which altitude was selected, a

higher altitude was associated with a later first

flight record. Again, this is as expected because it is

Fig. 2 Canonical variate (CVA) and principal coordinate (PCO) ordinations of the 29 aphid species based on presence/absence of

explanatory variables in the final models. (a) CVA ordination for species which � live year round on herbs (� group mean); }, live year

round on trees (4 group mean); & , alternate between herbs and trees (& group mean; see Tables 1 and 5). (b) CVA ordination for

species which � are entirely anholocyclic (� group mean); & , entirely holocyclic (& group mean); }, have both life-cycle types (4group mean; see Tables 1 and 5). (c) PCO ordination labeled according to the different life-cycle types (Table 5): �, anholocyclic (living

year round on herbs only), holocyclic (! living year round on trees only, or & alternating between herbs and trees) or having both life-

cycle types (} living year round on herbs only, living year round on trees only, or 4 alternating between herbs and trees).



likely to be colder at higher altitudes. However,

altitude is not significantly correlated with temperature

in the data set used here (Table 6). Associations between

longitude and first flight records were of mixed sign.

This might be expected in view of the lack of a sig-

nificant correlation between longitude and temper-

ature (Table 6). Rainfall is likely to cause mortality

through drowning and through preventing movement

(Harrington & Taylor, 1990), and therefore, the associa-

tion of higher rainfall with later first flight records is as

expected. The sign of the coefficient for the land-use

categories is variable. This is not surprising as different

species prefer different habitats. However, for the ele-

ven species for which urban land was selected, it was

always associated with earlier flights. Again, this may

be related to temperature, which is usually higher in an

urban environment. The tendency for more sea to be

associated with later flights is not surprising as the

presence of sea precludes the presence of aphid host

plants. It is more difficult to explain the tendency for

larger areas of arable land to be associated with later

flights, especially as the common cereal aphid species

(Rhopalosiphum maidis, R. padi and Sitobion avenae) are

included here. One possibility is that, as these species

can feed on noncultivated grasses as well as cereals, and

anholocyclic clones rely on noncultivated grasses to tide

them over between the harvesting of one cereal crop

and emergence of the next, these species are more

dependent on noncultivated grasses, which tend to

grow in noncrop areas, than they are on cereals. The

negative association of arable land area with first

flight records of noncereal species can be explained

on the basis that most of the arable area is accounted

for by cereals.

300

250

200

150

100

50

0

Julia

n D

ays

1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 29 9 9 9 9 9 9 0 0 0 0 0 0 0 0 0 0 06 7 7 8 8 9 9 0 0 1 1 2 2 3 3 4 4 5

55 005 05 05 05 05 05 05 0Year

Predicted valuesObserved valuesTrend (m = −0.250, R2 = 0.21)

Model: Julian Day =137.976

+ 0.000023574

+ 0.0000390578

+ 0.0000287559

+ 0.000102947

3.36069

6.44658

0.0524365

0.108988

0.0590551

+

+

+

−

−

∗

∗

∗

∗

∗

∗

∗

∗

∗

ARABLE

GRASS

SEA

WATER

C30DAY

C30P60DAY

DECPRN

FEBRN

NOVPRN

Fig. 3 Observed and predicted dates of first flight of Myzus persicae at Rothamsted and projected values on the basis of climatic changes

predicted by the HADCM3 A1FI scenario.



Tab

le6

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168

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229�

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80.

206

0.33

71

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80.

318

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304

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338

0.35

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A



It would be useful to be able to include host plant

data among the explanatory variables but, while pre-

sence/absence data could potentially be collated

for some relevant species, there are no suitable pan-

European data sets on plant abundance.

Variance accounted for in relation to life cycle and hostplant usage traits

The difference in the strength of the models, and the

discriminations achieved by the CVA and principal

coordinate analyses, encourage pursuit of groupings

of traits that may help to predict responses of aphids

to environmental changes. Models for those species

which spend the whole of their life cycle on herbs

generally accounted for a greater percentage of the

variance in first flight record than did models for

species which spend the whole of their life cycle on

trees. This suggests that the list of explanatory variables

lacks variables which are important in explaining the

first flight records of year-round tree-dwelling species,

although it is difficult to suggest what these variables

might be, as they apparently do not affect those species

which, up to the time of first flight, feed on trees but

then migrate to herbs (‘HH het’ in Table 5). First flight

dates of aphids which overwinter in the egg stage

(holocyclic) and those aphids which overwinter in the

active stages (anholocyclic) would be expected to be

affected to different degrees by environmental condi-

tions in winter. For example, eggs are far more tolerant

of low temperatures than are active stages. Eggs are

also likely to be less affected than active stages by

rainfall. First flight dates of monoecious and hetero-

ecious species would be expected to be affected

to different degrees by environmental conditions

because there is an element of genetic programming

of winged forms in the latter in order to ensure transfer

to the summer host whereas, in the former, winged

forms are produced mainly in response to host plant

quality and degree of crowding of the aphids (Dixon,

1998).

Trait-based classifications have been used with some

success to predict responses of plants to environmental

changes (Bazzaz, 1990; Paruelo & Lauenroth, 1995;

Condit et al., 1996; Diaz & Cabido, 1997; Cornelissen

et al., 2001; Dormann & Woodin, 2002; Epstein et al.,

2002). For insects, Landsberg & Stafford Smith (1992)

used functional groups to predict impacts of climate

change on outbreaks of agricultural and forest pests, but

there are few, if any, other examples. Such general-

izations have the potential to save a great deal of time

through avoiding the need for long-term data or ex-

tensive experimental work in order to predict responses

of every species of interest.

It might be expected that models constructed for a

single site will account for more variance in date of first

flight record than will pan-European models because of

the much greater variation in life-cycle type and host

plants that will be seen on the pan-European compared

with the local scale. For example, several species are

entirely holocyclic in northern Europe, where it is too

cold for survival of the active stages in winter. In such

cases, first winged forms occur after egg hatch and a

number of genetically programmed wingless genera-

tions on a woody host. In southern Europe, the same

species may be anholocyclic (continuously parthenoge-

netic), first flight being determined largely by host plant

quality and by the degree of aphid crowding (Dixon,

1998), and occurring potentially as soon as it is warm

enough for flight. Presence of primary host plants (the

woody winter hosts) also influences the balance of

holocycly and anholocycly in a region. Even where

temperatures permit anholocycly, a sufficiency of pri-

mary hosts may lead to a predominance of holocycly.

Single site models may thus better predict the impacts

of environmental variables at those sites than will pan-

European models. Development of site-specific models

using the EXAMINE data set is certainly warranted, but

is beyond the scope of this paper. However, as an

example, M. persicae has been studied at Rothamsted,

UK and, here, 78% of the variance in first flight records

from 1965 to 2000 is accounted for by mean January and

February temperature alone. The pan-European REML

model accounts for 54% of the variance in first flight

record over the same years.

The REML methodology has advantages over multi-

ple linear regression, as described in the ‘Materials and

methods’. Multiple linear regression has been used to

find associations between similar (but not identical)

explanatory variables to those used here, and first flight

records of M. persicae (Cocu et al., 2005). The variance

accounted for in pan-European models using multiple

linear regression was generally similar to that using

REML but, because of the improved variance structure

using REML, the results from REML are likely to be

more robust. Artificial neural networks (ANNs) have

also been used (Cocu et al., 2005). Here, a slightly

greater percentage of the variance in the first flight

record of M. persicae was accounted for than when using

the other methodologies. ANNs might provide better

predictions than REML (or multiple linear regression)

but ANNs have the disadvantage that it is difficult to

tease out the contribution of individual variables.

Scenario analyses

Many climate change models predict an increase in the

frequency of extreme weather events. The failure of the



models using scenario data to predict the extremes in

date of first flight records is probably a result of the use

of monthly, rather than daily, weather data. Monthly

mean data fail to account for extreme conditions that

will occur over much shorter time scales and probably

influence dates of first flight. For example, an unusually

low temperature occurring on a single day might have

a devastating effect on a population of aphids over-

wintering in the active stages and lead to a greatly

delayed record of first flight.

In all species where meteorological variables feature

in the models, model runs show that under the

HADCM3 A1FI scenario, earlier records of first flight

may be expected in the future. The advance varies

according to species and site, the range probably being

indicative of pan-European variation generally. Site-

specific models are likely to reflect better the expected

situation at individual sites. The expected advance in

date of first flight record is somewhat less than in cases

where individual species have been looked at for in-

dividual sites. For example Harrington et al. (1995),

using a model which only considered relationships

between winter temperature and first flight record of

M. persicae at Rothamsted, showed that the expected

advance in first flight record would be approximately

14 days for every degree centigrade rise in temperature.

Zhou et al. (1995) using a different index of phenology

predicted an average advance of 9 days in the United

Kingdom for the same species and temperature in-

crease. Under the HADCM3 A1FI scenario, a tempera-

ture increase of approximately 0.0375 1C/yr is expected

over the next 50 years, leading to an advance in first

flight record of 0.53 days/yr (14� 0.0375) in the Har-

rington et al. (1995) study and 0.34 days/yr (9� 0.0375)

in the Zhou et al. (1995) study, compared with

0.25 days/yr in the case of the REML analysis reported

here. The difference may be related to the failure to

consider land use in the former models, but failure to

consider land use change in the latter model may also

have an impact.

Statistical considerations

REML analysis can be considered as regression analysis

with improved modelling of variance structure. The

limitations of regression therefore still apply: relation-

ships with explanatory variables are based on correla-

tion and are not necessarily causative; correlation

between explanatory variables may make interpretation

difficult; and it is difficult to model reliably interactions

between variables without very large amounts of data.

Furthermore, the structure of the land-use variables

means that not all of these variables can be included

in the data set, which may lead to further problems of

interpretation. Cocu et al. (2005) chose to omit the sea

component of land use from their set of explanatory

variables. However, a large sea component is expected

to decrease aphid numbers for all species, and this

decrease can then only be modeled via a combination

of all other land-use variables. For this reason, we

decided to omit the less prevalent categories of land

use where fitting and interpretation were likely to be

less reliable.

Some caution is required in the construction and

interpretation of predictions under climate change sce-

narios. The predictions for climate change scenarios are

constructed from the selected subsets of climatic and

land-use variables for each aphid species. However,

land use is also related to climate, and might reasonably

be expected to adapt to climate change, but this has not

been taken into account in the predictions, where land

use has been held constant. In addition, it is perilous to

make predictions outside the current geographic range

of a species.

The extent to which any changes in aphid phenology

will translate into changes in the pest status of aphids

will depend partly on how the phenologies of their crop

hosts change. In the case of annual spring planted

crops, planting dates depend greatly on soil condition

in spring, and this is affected particularly by winter and

spring rainfall, less so by temperature. With aphids, it is

probably the other way around. There is much more

uncertainty over future patterns of rainfall than there is

over temperature, and it is hence difficult to predict

how crop phenology will change. In the case of potatoes

and sugar beet in the United Kingdom, unpublished

data suggest that planting dates are not advancing as

fast as aphid first flight dates. If this is the case, aphids

may arrive when crops are at an earlier and more

susceptible growth stage.

Acknowledgements

The authors are grateful to all those involved in funding, run-ning and maintaining the suction traps throughout Europe andto all involved in identifying the aphids sampled. RothamstedResearch receives grant-aided support from the Biotech-nology and Biological Sciences Research Council of the UnitedKingdom.

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Environmental change and the phenology of European aphids

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