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Journal of Applied
Ecology
2002
39
, 31–42
© 2002 British
Ecological Society
Blackwell Science Ltd
Brown locust outbreaks and climate variability in southern
Africa
MARTIN C. TODD*, RICHARD WASHINGTON†, ROBERT A. CHEKE‡ and
DOMINIC KNIVETON§
*
Department of Geography, University College London (UCL), 26 Bedford Way, London WC1H 0AP, UK;
†
School
of Geography, University of Oxford, Mansfield Road, Oxford OX1 3TB, UK;
‡
Natural Resources Institute, University
of Greenwich, Central Avenue, Chatham Maritime, Kent ME4 4TB, UK; and
§
School of Chemistry, Physics and
Environmental Sciences, University of Sussex, Brighton BN1 9QJ, UK
Summary
1.
The brown locust
Locustana pardalina
is a major agricultural pest in southern
Africa, with populations periodically reaching plague proportions. Management
and control would benefit from a predictive capacity at seasonal time scales, as yet
unavailable.
2.
The results of a study into the dynamics and potential predictability of locust popu-
lations in southern Africa are presented here. The number of districts reporting locust
control measures was used as a proxy for swarming brown locust populations.
3.
Spectral analysis of the annual number of brown locust infestations over southern
Africa revealed dominant periodicity at 17·3 years. The data were low-pass filtered and
the low-frequency and high-frequency components were retained. The low-frequency
component led the observed 18-year cycle in southern African rainfall by about 3 years,
and was therefore likely to reflect endogenous controls on populations.
4.
Variability in the interannual high-frequency component of brown locust in-
festations was strongly related to rainfall over the Karoo and Eastern Cape regions
of South Africa. The highest correlations were with rainfall over the 12 months
prior to the locust season (
r
= 0·64) and in particular with rainfall during December
(
r
= 0·55).
5.
Evidence is presented that the high-frequency component is related to the Pacific El
Niño/Southern Oscillation (ENSO) and that high-frequency locust activity is abnor-
mally high (low) during La Niña (El Niño) events.
6.
The high-frequency component of locust activity correlates positively and
negatively, respectively, with sea-surface temperatures over the tropical western and
eastern Pacific Ocean many months in advance of the locust season. Activity also
correlates positively (negatively) with sea-surface temperatures over the south-west
Indian Ocean and the Southern Ocean (west and north-west Indian Ocean). These
relationships occur later than those in the Pacific, developing in the austral winter and
peaking in early summer. This pattern of correlations and the associated atmospheric
circulation anomalies is consistent with ENSO-related and non-ENSO related patterns
of climate variability.
7.
The results suggest that there may be considerable scope for future development
of models for the seasonal prediction of brown locust activity in which high-frequency
variability is related to climatic indices.
Key-words
:
ENSO, Kalman filter, population dynamics, seasonal forecasting, sea-
surface temperatures
Journal of Applied Ecology
(2002)
39
, 31–42
Correspondence: Martin Todd, Department of Geography, University College London (UCL), 26 Bedford Way, London
WC1H 0AP, UK (fax + 44 20 76794293; e-mail m.todd@geog.ucl.ac.uk).
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et al.
© 2002 British
Ecological Society,
Journal of Applied
Ecology
,
39
,
31–42
Introduction
The brown locust
Locustana pardalina
(Walker 1870) is
a major agricultural pest in much of southern Africa
(SA), south of about 20
°
S. It has been described as the
most important agricultural pest in South Africa (Lea
1953), where its main outbreak areas are in the semi-
arid Karoo region (Botha 1969; Centre for Overseas
Pest Research 1982). Population fluctuations can be
dramatic, with plagues spreading from the source
region into neighbouring Namibia, Botswana and
Zimbabwe. Despite a long history of research into the
dynamics of population variability, there remain no
definite predictions sufficiently far in advance to plan
anti-locust campaigns. This has resulted in the applica-
tion of insecticides over large areas during outbreaks
of the swarming phase (Nailand & Hanrahan 1993). In
this context there are potentially significant benefits to
understanding the controls on population dynamics
with a view to developing a predictive capacity.
The brown locust life cycle is well understood (Price
1988; Nailand & Hanrahan 1993). Egg hatching is a
complex process involving quiescence and diapause
but is stimulated by rainfall (Matthée 1951). When
hatching is successful and widespread, with the result-
ant nymphs at high enough population densities, the
insects change phase from the solitary to the gregarious
condition and occur in swarms. Swarming adults con-
gregate at oviposition sites covering up to 100 ha in the
outbreak areas, where egg pods are laid in loose, dry
soil often shaded by small Karoo bushes. The eggs
occur in two forms: (i) those that hatch within 10 –
20 days given adequate moisture, e.g. after 15 – 25 mm
of rain (Smit 1939) or, if conditions are unsuitable,
become quiescent and hatch after some months; and
(ii) those that enter diapause for 1–3 or more months.
Both kinds of egg may be present in the same pod and
are drought resistant. The ability of brown locust eggs
to become quiescent or to enter diapause contrasts
with the lack of such adaptations in the eggs of the
desert locust
Schistocerca gregaria
(Forsk), but is sim-
ilar to the condition found in the Senegalese grass-
hopper
Oedaleus senegalensis
(Krauss), a major pest in
the Sahel region of West Africa (Fishpool & Cheke
1983; Cheke 1990). The brown locust has five nymphal
instars or occasionally four in males. The hopper
period of solitary locusts lasts 21–38 days, and at least
42 days for the gregarious phase. In the latter phase
hoppers tend to be larger than in the former (Centre for
Overseas Pest Research 1982). Years of high rainfall can
produce three generations in one season (September–
April) and four in a year. Under drought conditions
the eggs can remain dormant for up to 15 months
(Matthée 1951). As such the brown locust is extremely
well adapted to the highly variable climate of the Karoo
region. The relationship between locust populations
and climate has long been noted. Early work of Du
Plessis (1938), Smit (1941) and Lea (1958, 1968) noted
a correlation with rainfall, and comprehensive recent
studies by Steedman (1990) and Nailand & Hanrahan
(1993) noted a positive (negative) correlation between
brown locust swarming during high summer and the
early summer (previous winter) rainfall.
The region of SA of interest to this study exhibits a
pronounced zonal climate gradient, with arid condi-
tions in the west and humid conditions in the east. Over
much of north-eastern SA there are distinct wet (sum-
mer) and dry (winter) seasons associated with the
annual cycle in the meridianal position of the Inter-
tropical Convergence Zone (ITCZ). In the Karoo and
Eastern Cape regions rainfall displays a bimodal dis-
tribution, with peaks in the transition seasons of spring
and autumn (Tyson 1986). The western parts of the
Northern and Western Cape provinces experience
a winter rainfall maximum. The climate of SA is
known to show pronounced variability at a range of time
scales from intraseasonal (Todd & Washington 1999),
through interannual (Jury 1997; Nicholson & Kim
1997; Rocha & Simmonds 1997) to decadal and multi-
decadal (Tyson 1986; Folland
et al
. 1999). Interannual
variability is particularly high in the drier region,
including the Karoo where the coefficient of variation
exceeds 40% (Tyson 1986). For a review of rainfall
variability in SA see Mason & Jury (1997).
That the climate system behaves as a coupled ocean /
atmosphere system, through the exchange of energy,
mass and momentum, is a dominant paradigm in con-
temporary climatology. The behaviour of the atmos-
phere is dependent on the ocean, and vice versa. El
Niño/Southern Oscillation (ENSO), the dominant
mode of global interannual climate variability, exerts
considerable influence over SA rainfall during the aus-
tral summer and is responsible for modulating the
extreme dry and wet years. A number of studies have
documented the development of positive (negative)
sea-surface temperature (SST) anomalies in the equa-
torial and northern (southern) Indian Ocean during
ENSO warm/El Niño events. During ENSO cold/La
Niña events a reversal of this pattern is observed. It has
been hypothesized that these SST anomalies modulate
the large-scale structure of the atmosphere. This occurs
through adjustments to the zonal structure of regions
of atmospheric convergence and divergence in the tropics
(the Walker circulation), and thus the large-scale con-
vergence of low-level moisture (necessary for rainfall)
in the SA region (Goddard & Graham 1999). During
ENSO warm events (El Niño) the rising limb of the
African Walker cell and associated rainfall is displaced
eastward into the Indian Ocean, resulting in anomalously
dry conditions over SA (Tyson 1986; Mason & Jury
1997; Goddard & Graham 1999; Reason
et al
. 2000).
In addition, Rocha & Simmonds (1997) and Preston,
Washington & Todd (2000) demonstrate the importance
to SA rainfall of ocean/atmosphere variability in the
Indian Ocean region that is independent of ENSO.
The coupling of the ocean and atmosphere provides
the physical basis for seasonal forecasting of climate
anomalies up to several months in advance (Murphy
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33
Brown locust
outbreaks and
climate variability
© 2002 British
Ecological Society,
Journal of Applied
Ecology
,
39
,
31–42
et al
. 2001). Seasonal forecasting is based on notions
that (i) the lower boundary forcing of the atmosphere,
most notably the state of the ocean (commonly repre-
sented by SST), evolves relatively slowly and as such is
predictable (because SST have a significant degree of
persistence from one month to the next), and (ii) the
atmosphere responds in a predictable manner to this
component of forcing. Therefore, SST in one season
can have an impact on the atmosphere in subsequent
seasons, thereby providing the basis for probabilistic
seasonal predictions of the behaviour of the atmos-
phere (often rainfall and/or temperature). Remote SST
anomalies, for example in the eastern tropical Pacific
associated with ENSO, may also take several months
to affect the atmosphere over SA, providing the lead
times needed for predictability. A number of studies
have recently demonstrated the potential for seasonal
forecasting of climate or climate-related variables over
SA, based at least in part on SST in the remote Indian
and Pacific Oceans (Thiaw, Barnston & Kumar 1999;
Washington & Downing 1999; Martin, Washington &
Downing 2000).
In this context, the primary aim of this study was to
further our understanding of the dynamics and pre-
dictability of brown locust populations in SA, through
analysis of a long-term data set of reported locust out-
breaks. Specifically, the following questions were
addressed. (i) What is the dominant periodicity of
locust outbreaks? (ii) To what extent is variability in
locust outbreaks related to exogenous climatic factors?
(iii) To what extent is the variability in locust outbreak
intensity over SA predictable from climatic indicators?
Materials and methods
One of the major problems facing research into the
behaviour of the brown locust is a lack of direct popu-
lation estimates over the extended areas and time peri-
ods necessary to develop generalized population
models. Accordingly, we were limited to indirect esti-
mates or indices, for which we used the number of mag-
isterial districts (D) in South Africa, Botswana and
Namibia in which brown locust control activity took
place in a given year, over the period 1947–98 (Price &
Brown 2000; M. E. Kieser, personal communication;
see also Milton, Davies & Kerley 1999 for patterns
since 1797). Whilst this is a coarse index of actual locust
numbers it is a useful indicator of the year-to-year vari-
ability, despite the potential effects of control measures
on population numbers. Similar data sets have been
used for the brown locust (Nailand & Hanrahan 1993)
and the desert locust (Cheke & Holt 1993). Although
the data do not describe the precise timing of brown
locust infestations, observations suggest that swarms
tend to occur in the mid- to late wet season (January–
March) after populations have grown sufficiently
(Steedman 1990; Kellner & Booysen 1999). Hereafter,
we use the term ‘locust season’ to refer to this early part
of the calendar year.
Periodicity of brown locust outbreaks was analysed
by singular spectral analysis (SSA) of the locust data.
On the basis of the SSA results, the low-frequency
(LF) component of brown locust variability (with
periodicities > 11 years; D
LF
) was separated from the
high-frequency (HF) component (with periodicities
< 11 years; D
HF
), by means of an integrated random
walk Kalman filter. The Kalman filter (described fully
in Young
et al
. 1991; Chatfield 1992) fits a smooth line
through a time series and is known to be less vulnerable
to large swings resulting from outliers in the observa-
tions than many simpler methods. The filtered data
represent the LF component of variability in D and can
be considered to represent variability at decadal time
scales. The LF component was then subtracted from
the raw time series to leave the HF component (D
HF
),
indicative of variability at interannual time scales.
Information on monthly rainfall (R) and near sur-
face air temperature over the same period was obtained
from Hulme (1992) and Jones
et al
. (1999), respect-
ively, which provided observations over global land
areas on a grid at 2·5
°
latitude by 3·75
°
longitude and
5
×
5
°
resolution, respectively. Monthly anomalies of
SST in the Niño-3·4 region of the central equatorial
Pacific (5
°
N–5
°
S, 170–120
°
W ) were used as an index of
the state of the ENSO system. Gridded global fields of
key atmospheric and surface climatic variables (SST,
low-level winds and sea-level pressure), indicative of
ocean boundary forcing and the atmospheric circu-
lation, were obtained from the National Center for
Environmental Prediction (NCEP) Reanalysis data set
(Kalnay
et al
. 1996). This provides monthly data on a
2·5
°
global grid for the period 1948–present.
The relationship between D
LF
and low-frequency
climate variability was assessed by comparison of the
time series of D
LF
and low-frequency rainfall variabil-
ity (R
LF
) over the SA region. We used rainfall time
series (R
LF
) for early (OND) and late (JFM) summer.
These were the eigenvector time coefficients of the lead-
ing empirical orthogonal functions (EOF) of seasonal
rainfall. The EOF were selected on the basis that
they have loadings over the central interior of SA
(Washington 1998). In addition, we used the OND R
LF
over the Karoo (grid cell centred on 32·5
°
S, 26·25
°
E).
To assess the influence of climate on the high-
frequency component of brown locust variability, the
relationship of D
HF
and climate variables (R, temper-
ature, SST, sea-level pressure and low-level winds) was
analysed by means of correlation and composite ana-
lysis. In the former case the time series D
HF
was cor-
related with the time series of climate variables at each
grid cell in the global field. To assess predictability of
D
HF
from climate, the D
HF
time series was lagged by a
number of months. The aim of composite analysis was
to identify the characteristic structure of the ocean and
atmosphere associated with the major years of high
and low brown locust activity. The D
HF
data were
ranked and the five most extreme years of high and
low locust activity were identified. Mean anomalies
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34
M.C. Todd
et al.
© 2002 British
Ecological Society,
Journal of Applied
Ecology
,
39
,
31–42
of NCEP climate data were then calculated for these
samples at each grid cell and statistical significance
was tested using a
t
-test.
To evaluate the predictive strength of the relation-
ship between brown locust variability and rainfall, esti-
mates of D
HF
were derived from a linear regression with
preceding annual rainfall at certain grid cells over SA.
The skill of these ‘hindcasts’ was tested through a ‘jack-
knife’ procedure, involving 52 regression analyses. In
each case a single year’s data were omitted and its D
HF
value predicted from the regression of D
HF
and annual
rainfall derived from the remaining data. Given the
absence of serial autocorrelation in the data (see the
Results) this ensured that hindcasts were made using
independent data. The accuracy of the predicted D
HF
relative to the observed D
HF
was compared using the
correlation coefficient, mean bias and root mean squared
error (RMSE). The estimates were also compared in
terms of broad categories using the Heidke skill score
(Wilks 1995) and linear error in probability space
(LEPS) score (Potts
et al
. 1996). Three categories were
selected (above normal, normal and below normal),
defined by the appropriate tercile values of the D
HF
dis-
tribution. The Heidke and LEPS scores defined the
percentage improvement in the accuracy of estimate
classification into these three categories over a refer-
ence strategy with little ‘skill’, such as random guessing
or climatological persistence. The LEPS included a
weighting to account for the magnitude of errors
between class boundaries.
Results
Singular spectral analysis of the raw number of dis-
tricts reporting brown locust control (D) revealed a
dominant peak at 17·3 years, with lesser peaks at 3·7, 2·9,
10·4 and 7·4 years in decreasing order of importance
(Figs 1 and 2). The peaks at 17·3, 3·7 and 2·9 years
in the power spectrum were statistically significant
at the 0·05 level or higher, based on the Bartlett–
Kolmogorov–Smirnov test where the null hypothesis
maintains that the time series was white noise (Fuller
1976). Kalman filtering of the data indicated that the
variance of the high-frequency component of D was
approximately twice that of the low-frequency compo-
nent. The new derived time series of D
LF
and D
HF
are
shown in Fig. 1. Analysis of the autocorrelation func-
tion of D
HF
at various lags from 1 to 20 years revealed
no statistically significant correlation (data not shown).
Kalman filtering of the SA rainfall data [OND and
JFM EOF1 from Washington (1998) and OND rainfall
at grid cell centred on 32·5
°
S, 26·25
°
E] showed multi-
decadal variability. A pronounced 18-year cycle occurred
in JFM R
LF
and, although the periodicity of the two
OND R
LF
time series was less clear, there was some
evidence of periodicity near 18 years (Fig. 3). However,
the D
LF
cycle led both the JFM and OND R
LF
cycle by
about 3–7 years (Fig. 3). Our confidence in the phase
of this low-frequency variability was not large owing to
the short data series.
-
The highest correlations (up to 0·55) between D
HF
and
surface rainfall preceding the brown locust swarming
season occurred over a restricted area of the Karoo and
Eastern Cape region of South Africa (notably two grid
cells centred on 32·5
°
S, 22·5
°
E and 32·5
°
S, 26·25
°
E) in
December (Fig. 4). There were significant correlations
1950
–20
–40
No. districts reporting control (D)
100
80
60
40
20
0
1960 1970
Years
1980 1990 2000
Fig. 1. Time series of the number of magisterial districts (D)
in South Africa, Botswana and Namibia reporting brown
locust outbreaks: raw data (thick line), low-frequency
component (circles), high-frequency component (thin line).
1000
Power
5000
4000
3000
2000
Period (years)
52
17·3
7·4
10·4
5·8
4·0
4·7
3·5
3·1
2·7
2·5
2·3
2·1
Fig. 2. Spectral density of raw time series of the number of
magisterial districts recording brown locust outbreaks.
1950
–2
–1
0
Standard LF anomalies
1
2
1960 1970
Years 1980 1990 2000
Fig. 3. Standardized time series of the low-frequency (LF)
components of the number of magisterial districts recording
brown locust outbreaks (thick line), leading empirical
orthogonal function (EOF) of January–March southern
African rainfall (thin line), EOF1 of October–December
(OND) southern African rainfall (circles) and OND Karoo
(32·5°S, 26·25°E) rainfall (triangles).
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Brown locust
outbreaks and
climate variability
© 2002 British
Ecological Society,
Journal of Applied
Ecology
,
39
,
31–42
Jan Feb
Oct
Jan - Dec
Dec
Jul Aug
Fig. 4. Correlation coefficients (*100) of the high-frequency
component of the number of magisterial districts recording
brown locust outbreaks with Hulme (1992) rainfall at various
time lags. The significance level at 0·05% (0·01%) is 0·273
(0·326).
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M.C. Todd
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Ecological Society,
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Ecology
,
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,
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over a broader region of SA in October. Correlations of
D
HF
and austral winter rainfall were weak, although
locally significant (at the 0·05% level) positive correla-
tions occurred in July over the Western Cape province
and in August over the Eastern Cape province. Signific-
ant positive correlations also occurred with late summer
(January and February) rainfall some 10 –12 months in
advance of the locust season. As a result, the highest
statistical relationship (
r
= 0·64) was observed between
D
HF
and annual (January–December) rainfall over the
Eastern Cape region (32·5
°
S, 26·25
°
E). That annual
rainfall in the year leading up to the locust season
explained a substantial proportion (42%) of variability
in D
HF
provides potential for predictability using linear
regression (Table 1). To test the validity of the posterior
selection of a target cell at 32·5
°
S, 26·25
°
E, the same
procedure was conducted using rainfall data at all grid
cells surrounding it. For brevity only the ‘best’ and ‘worst’
results from surrounding cells are shown (Table 1).
The relationship of D and D
HF
with rainfall at the
cell centred on 32·5
°
S, 26·25
°
E, indicating that only the
HF component of locust populations had a strong rela-
tion to rainfall (Fig. 5). Correlation analysis with sur-
face temperature fields over the SA region revealed no
statistically significant correlations during any month
or season within one year preceding the wet season
(data not shown).
The extreme years of HF brown locust activity were
1985–86, 1950–51, 1970–71, 1963–64, 1971–72, of
which four corresponded to ENSO ‘cold’ events (La
Niña) in the Pacific (on the basis of January SST anom-
alies in the Niño-3·4 index). Strong SST anomalies
occurred throughout the previous year in these cases.
The extreme years of low brown locust activity were
1972–73, 1992– 93, 1990– 91, 1949–50, 1987– 88, of which
two (1972–73 and 1987–88) corresponded to major ENSO
‘warm’ conditions (El Niño). The events in the 1990s
coincided with the prolonged occurrence of moderate
El Niño conditions throughout the early 1990s.
Correlations between D
HF
and the Niño-3·4 index of
Pacific SST at various lags (Fig. 6) were statistically sig-
nificant (at the 0·05% level) for up to 12 months prior
to the brown locust season (assumed to occur in mid/
late summer). Highest correlations were observed with
the Niño-3·4 index during February–May (austral
summer/autumn, peaking at –0·43 in February, sig-
nificant at the 0·01% level) prior to the brown locust
Table 1. Accuracy assessment of hindcasts (n = 52) of the high-frequency component of brown locust populations estimated
from linear regression with preceding annual (January–December) rainfall at individual grid cells over southern Africa,
climatological persistence and random guessing
Hindcast estimation
method
Linear regression with
annual rainfall at cell
32·5°S, 26·25°E
Linear regression with
annual rainfall at cell
32·5°S, 22·5°E
Linear regression with
annual rainfall at cell
30°S, 30°E Climatology
Random
guessing
Correlation 0·6 0·37 0·00
Mean bias 0·14 0·05 0·18 0·0 0·0
RMSE 13·4 16·0 17·4 17·3 17·3
Heidke skill score 34% 16% –15% 0% 0%
LEPS 52% 18% –71% 0% 0%
No. of districts reporting control (D)
80
60
40
20
0500400300 600
Annual rainfall (mm)700 800 900
200
HF component of D
40
20
0
–20
60
(b)
(a)
500400300 600
Annual rainfall (mm)
700 800 900
Fig. 5. (a) The relationship between the number of
magisterial districts recording brown locust outbreaks and
annual rainfall (during the preceding year) centred on 32·5°S,
26·25°E; (b) as (a) but for the high-frequency (HF)
component of locust data.
J
–0·4
–0·5
Correction coefficient
–0·1
–0·2
–0·3
Month (prior to locust season)
FMAMJ J ASOND
Fig. 6. Time series of correlation coefficients of the high-
frequency component of number of magisterial districts
recording brown locust outbreaks and sea-surface temperature
anomalies in the Niño-3·4 region. The 0·05% (0·01%)
significance level is 0·273 (0·354).
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Brown locust
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climate variability
© 2002 British
Ecological Society,
Journal of Applied
Ecology
,
39
,
31–42
season (Fig. 6). The sign of the correlations indicated
that ENSO warm (cold) events generally preceded
years of below (above) average HF locust irruptions.
Correlation analysis of D
HF
and gridded SST at vari-
ous lags (Fig. 7a–i) indicated statistically significant
positive (negative) correlations (up to 0·5) in seasons
prior to the brown locust season over extensive regions
of the tropical western (eastern) Pacific. Negative
correlations (up to 0·5) also occurred over the extensive
regions of the western Indian Ocean from austral
winter onwards (feature A in Fig. 7d–i), associated
with positive correlations (up to 0·5) over the south-
west Indian Ocean (feature B in Fig. 7d–i). Broadly,
there was a north/south dipole in the correlation sign
over the north-west/south-west Indian Ocean. This cor-
relation structure in the Pacific and Indian Ocean basins
was consistent with a persistent ENSO signal, repres-
ented by correlations of the opposite sign in Fig. 7j.
The dipole structure of negative (positive) correla-
tions over the north-west and central southern (south-
west) Indian Ocean (features A and B in Fig. 7d–i)
evolved from the austral winter season and peaked in
strength during early summer (December; Fig. 7i). An
arc of negative correlations extended from the north-
west Indian Ocean to the subtropical southern Indian
Ocean from winter onwards (feature A) and the highest
correlations moved southwards to lie at 30
°
S, 55
°
E in
December. Positive correlations (feature B) propagated
westward from the south-west Indian Ocean, with the
highest correlations in this region (up to 0·5) located
immediately south of SA in the Southern Ocean (at
40
°
S) during December. An index of SST over this
Fig. 7. (a) to (i) Correlation of sea-surface temperature anomalies (at various negative lags) and the high-frequency component of the number o
f
magisterial districts recording brown locust outbreaks. Positive (negative) correlations are shown as solid (dashed) contours. The contour interval is 0·1 and
the zero contour is omitted. (j) Correlation of coincident NCEP reanalysis sea-surface temperature anomalies and Niño-3·4 sea-surface temperature
anomalies during October–December. The contour interval is 0·2 and the zero contour is omitted. The 0·05% (0·01%) significance level is 0·273 (0·354).
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,
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region (37·5–42·5
°
S, 11·5– 22·5
°
E) for the OND season
had a correlation of 0·5 with D
HF
. Throughout the
austral early summer period there were negative corre-
lations between D
HF
and SST over the subtropical
south Atlantic, centred on 20
°
W, 3 5
°
S (feature C in
Fig. 7g–i). The correlations with SST in the Southern
Ocean (south of SA), the south-west Indian Ocean and
south-east Atlantic were higher for locust activity than
for the Niño-3·4 index (Fig. 7j), suggesting that in these
regions the observed SST structure related to D
HF
may
not be entirely ENSO related.
In December prior to high D
HF
events, associated
with La Niña conditions, an anomalous continental
low was located over SA (Fig. 8a). An anomalous SLP
high was centred over the south-west Indian Ocean
near 50
°
S, 50
°
E (Fig. 8a). These features led to anom-
alous low-level easterlies from the subtropical south-
west Indian Ocean peaking at 40
°
S (Fig. 8b). During
low D
HF
events, associated with El Niño conditions,
these anomalies were reversed and the moist easterlies
were weakened over SA. The inference that such mech-
anisms are directly related to rainfall is supported by
the close similarity with the correlation structure
between these fields and December rainfall over the
Karoo and Eastern Cape region (31–33
°
S, 22–26
°
E)
(data not shown).
Discussion
Understanding the nature of brown locust populations
over SA so that effective control measures can be
implemented, could potentially result in considerable
180
(a)
(b)
10N
10S
20S
30S
40S
EQ
120W 60W 60E 120E 180W0
Fig. 8. (a) Composite mean anomalies of December sea-level pressure (hPa) for the uppermost minus lowermost 5 years of the
high-frequency component of the number of magisterial districts recording brown locust outbreaks. Contour interval is 1 hPa
(zero contour omitted) and positive (negative) anomalies are solid (dashed) lines. Shaded areas are significant at 0·05% level. (b)
Composite mean anomalies of December low-level (850 hPa) wind vectors (m s–1) for the uppermost minus lowermost 5 years o
f
the high-frequency component of the number of magisterial districts recording brown locust outbreaks (shaded areas are
significant at 0·05% level). Unit length vector equal to 3 m s–1.
JPE_691.fm Page 38 Thursday, January 17, 2002 2:44 PM
39
Brown locust
outbreaks and
climate variability
© 2002 British
Ecological Society,
Journal of Applied
Ecology
,
39
,
31–42
benefits to agriculture but has remained a problem.
First, it is likely that the insect’s capacity for rapid
population growth represents the interaction of both
endogenous and exogenous factors. Secondly, the
development of mathematical population models is
difficult because of the locust’s phase change, which
can result in locusts being barely noticeable as solitary
populations in one generation and then gregarious
swarms in the next. Finally, there is a lack of quantit-
ative field data of actual population numbers. In
comparison to the desert locust, the population
dynamics of the brown locusts have received relatively
little attention despite the potential benefits.
This study has focused on analysing the nature of
brown locust populations and the possible exogenous
control exerted by climate. From this we were able to
assess the potential predictability of populations of
brown locust, on the basis of the evolution of the
climate system. The data set used was a proxy index of
brown locust populations (D) in which the precise
nature of the relationship to actual locust numbers
cannot be specified, although we assume that the
data were indicative of late austral summer swarming
populations.
The dominant 17·3-year cycle (Fig. 2) in brown
locust populations is substantially longer than those
identified previously (Lounsbury 1915; Lea 1968,
1972) but close to the 16-year cycle identified by Cheke
& Holt (1993) for the desert locust in West Africa. A
key question is what drives this LF periodicity. There is
evidence that the climate of SA experiences decadal
variability dominated by an 18-year periodicity
(Mason & Jury 1997), possibly related to global low-
frequency SST anomalies (Washington 1998; Folland
et al
. 1999). However, as the LF component of the D
time series leads that of SA rainfall by about 3–7 years
(Fig. 3), it is unlikely that LF variability in brown
locust populations results from decadal variability
in rainfall. Although it is possible that other climate
variables and/or interaction with pathogens may be
involved, it is likely that the observed LF variability
in brown locust populations may be an expression
of endogenous controls. In any case, the strong LF
cyclicity in brown locust outbreaks suggests that about
one-third of the total variance may be predicted on the
basis of a 17·3-year oscillation.
Working with logistic equations governing popula-
tion growth rates, May (1974, 1976) suggested that for
populations with particular intrinsic rates of genera-
tional population increase (r, where 2·685 < r < 2·692)
population numbers can exhibit stable cyclic behaviour
with ‘period doubling’. The spectral peaks of D (Fig. 2)
show little evidence of this, perhaps indicating that the
brown locust has chaotic ‘boom and bust’ population
dynamics, characteristic of higher growth rates and
determined by endogenous factors (May 1974, 1976).
Further research is therefore required into the precise
cause of the low-frequency cyclicity in brown locust
outbreaks.
The dominant proportion of total variance of D is
contained in the HF component and is of primary
interest in terms of interannual variability and predict-
ability of locust populations. There is little temporal
autocorrelation in the HF component (data not
shown). This is suggestive of ‘boom and bust’ dynamics,
although our subsequent analysis suggests that there
is substantial exogenous control of brown locust popu-
lation numbers. The absence of serial autocorrelation
is in contrast to the desert locust over West Africa,
where positive autocorrelation at 1 year is significant
(Cheke & Holt 1993). It is also possible that popu-
lations are dependent on some other unidentified
precedent population characteristic. Price (1988) sug-
gests that swarms arise after the build-up of the solitary
phase in the previous year. Unfortunately, our data are
best seen as an index of the swarming populations and
thus do not support investigation of this hypothesis.
The raw brown locust data exhibit only a weak rela-
tionship with rainfall, characterized by heteroscedasity
(Fig. 5a). Cheke & Holt (1993) observed a similarly
heteroscedastic relationship between rainfall and
desert locust populations in West Africa, and found
that simulations of populations using a logistic model
with high growth rates (characteristic of chaotic
dynamics related to endogenous factors) revealed sim-
ilar patterns. For brown locusts, our results indicate
that much of the scatter in the raw data/rainfall rela-
tionship can be removed by separating the variability
at low-frequency (decadal) time scales from the high-
frequency (or interannual) component, and treating
the latter separately (Fig. 5b).
High-frequency brown locust variability is most
strongly associated with December rainfall over the
Karoo region and to a lesser extent the Eastern Cape
region (Fig. 4). This confirms that brown locust out-
breaks in the wider SA region originate from a relat-
ively restricted source region where locusts are known
to breed, and that this process is most sensitive to rain-
fall in the early summer period, particularly December
rainfall. In addition, we observe significant correla-
tions of DHF with rainfall over the same region during
the previous late wet season. As such, a substantial pro-
portion (49%) of DHF variance can be explained from
annual rainfall prior to the locust season.
We find no evidence of a connection between brown
locust irruptions and previous austral winter rainfall
(Nailand & Hanrahan 1993; Kellner & Booysen 1999)
nor temperatures (Kellner & Booysen 1999) over SA.
Much of the interannual variability unrelated to rain-
fall may therefore be endogenous. It is also important
to note that there is evidence that locust population
breeding regions can change over time, possibly as a
result of changes in local vegetation (L.J. Rosenberg,
personal communication). As our study is based on
a long-term data set, the results may reflect histor-
ical conditions rather than those in the present day, at
least in regions where ecological changes have been
pronounced.
JPE_691.fm Page 39 Thursday, January 17, 2002 2:44 PM
40
M.C. Todd et al.
© 2002 British
Ecological Society,
Journal of Applied
Ecology, 39,
31–42
That the high-frequency component of the number
of districts reporting brown locust control (DHF) exhibits
a strong correlation with both annual and, in par-
ticular, preceding December rainfall over a relatively
small region may indicate that there is scope for develop-
ing a predictive capacity at interannual time scales.
First, monitoring of rainfall in real time may facilitate
short lead-time predictability of likely irruption rates in
the remainder of the wet season following December,
using a simple linear regression. The results (Table 1)
show that hindcasts based on regression of DHF and
rainfall over the Karoo (32·5°S, 26·25°E) are accurate
relative to (i) climatological persistence or random
guessing and (ii) hindcasts based on rainfall in neigh-
bouring grid cells (highlighting the importance of this
region of the Karoo). In practice, such predictions may
facilitate more efficient planning, preparation and
resource allocation for subsequent locust control.
In addition, forecasts with longer seasonal lead
times may be possible. There is substantial evidence
that the high-frequency component of brown locust
populations is abnormally high (low) during La Niña
(El Niño) phases of the Pacific ENSO system. This is
consistent with the documented relationship between
ENSO and SA rainfall. In addition, the spectral peaks
at 3·7 and 2·9 years identified from spectral analysis of
the raw locust data are within the interannual compon-
ent of the ENSO signal (Allan 2000). High-frequency
locust variability shows significant associations with
SST over extensive regions of the Pacific and Indian
Oceans in the seasons prior to the locust plague season
(Fig. 7a–i). In the tropical Pacific there is a clear east/
west dipole of negative/positive DHF/SST correlations
representing the major centres of action of ENSO. In
addition, SST anomalies develop in the Indian and
southern Atlantic Oceans some months later than
those in the Pacific. This space/time structure is highly
characteristic of ENSO-related variability in the major
ocean basins (Fig. 7j). A north/south dipole in cor-
relations over the western Indian Ocean similar to that
observed here, during the JAS and OND season (lag-
ging the peak SST anomalies in the Pacific), has been
noted in composites of major ENSO events (Nicholson
& Kim 1997; Reason et al. 2000). Given that the SST
structure over much of the Indian Ocean lags that in the
Pacific, there is scope to develop a statistically based
prediction of the former on the basis of canonical cor-
relations (Goddard & Graham 1999).
An important question is whether the evolving SST
structure in the south-west Indian Ocean (Fig. 7a–i) is
typical of that associated with ENSO. That correla-
tions with SST in the south-west Indian Ocean, the
Southern Ocean (south of SA) and south-east Atlantic
(Features A, B and C, respectively, in Fig. 7i) are notably
higher for locust activity than for the Niño-3·4 index
(Fig. 7j) suggests that locust activity may be related to
ENSO but that the specific structure of SST in the
oceans immediately surrounding SA may also be cru-
cial in determining the climate and response of brown
locusts. There is growing evidence of patterns of Indian
Ocean SST that are independent of ENSO (Rocha &
Simmonds 1997; Preston, Washington & Todd 2000).
Although it is beyond the scope of this paper to
establish the physical mechanisms by which the evolu-
tion of the ocean thermal structure influences SA cli-
mate, it is notable that early summer sea-level pressure
anomalies associated with DHF extremes (Fig. 8a)
resemble characteristics of ENSO-related modulation
of the atmospheric Walker circulation noted by Reason
et al. (2000). The composite mean low level (850 hPa)
wind anomaly field associated with extreme DHF years
(Fig. 8b) is broadly consistent with both observed sur-
face wind anomalies associated with La Niña events
(Reason et al. 2000) and with general circulation model
simulations of the effect of ENSO-related Indian Ocean
SST anomalies (Goddard & Graham 1999). Thus, dur-
ing La Niña events and periods of high DHF activity,
anomalous easterlies flow into SA from the south-west
Indian Ocean (the dominant moisture source for SA)
advecting large quantities of moisture over SA, facili-
tating the development of convective rainfall systems
(Tyson 1986). Thus, in accordance with previous work,
our results indicate that it is the combination of SST
and atmospheric circulation anomalies that dictates
the nature of climate anomalies in the SA region, to
which there appears to be a consistent response in the
HF component of locust infestations.
In this study we have identified that an index of
annual brown locust infestations over SA consists of
a low-frequency component, possibly controlled by
endogenous factors, and a high-frequency component,
strongly related to rainfall in the Karoo (and Eastern
Cape) regions. About one-third of the total variance
can be represented by the 17-year cycle, while much of
the remaining high-frequency variability can be related
to indices of the evolution of the large-scale climate sys-
tem. As such, there appears to be considerable scope
for developing statistical models for seasonal predic-
tion of brown locust activity many months in advance.
Such forecasts may be useful to optimize resource allo-
cation and preparation for locust control activities. The
key predictor indices are likely to be SST in the tropical
Pacific and western Indian Oceans, the south-west
Indian Ocean and the Southern Ocean immediately
south of SA, indicative of both ENSO and non-ENSO
modes of variability. December rainfall over the Karoo
region is an important control on locust populations
and thus may provide a valuable late ‘check’ on the
likely accuracy of any seasonal forecasts. Seasonal fore-
casting of the high-frequency component of brown
locust infestations (rather than climate variables as in
previous work) would certainly represent a novel develop-
ment in this field.
Acknowledgements
The authors are grateful to the UCL and the University
of Oxford for support. NCEP reanalysis data were
JPE_691.fm Page 40 Thursday, January 17, 2002 2:44 PM
41
Brown locust
outbreaks and
climate variability
© 2002 British
Ecological Society,
Journal of Applied
Ecology, 39,
31–42
obtained from the National Centre for Atmospheric
Research. The Niño-3·4 time series of Pacific
Ocean SST anomalies was obtained from the
NOAA Climate Prediction Center http://
www.cpc.ncep.noaa.gov/data/indices. R.A. Cheke is
also grateful for support from programme develop-
ment funds (NRI project ZA0394) of the Crop
Protection Programme of the UK Department for
International Development (DFID) for the benefit
of developing countries. The views expressed are not
necessarily those of DFID. Thanks also to Dr Jane
Rosenberg of the NRI for helpful comments.
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