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Brown locust outbreaks and climate variability in Southern Africa


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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. The results of a study into the dynamics and potential predictability of locust populations in southern Africa are presented here. The number of districts reporting locust control measures was used as a proxy for swarming brown locust populations. 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. Variability in the interannual high‐frequency component of brown locust infestations 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). 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 abnormally high (low) during La Niña (El Niño) events. 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. 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.
Content may be subject to copyright.
Journal of Applied
, 31–42
© 2002 British
Ecological Society
Blackwell Science Ltd
Brown locust outbreaks and climate variability in southern
Department of Geography, University College London (UCL), 26 Bedford Way, London WC1H 0AP, UK;
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
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
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.
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.
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 (
= 0·64) and in particular with rainfall during December
= 0·55).
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.
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.
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.
ENSO, Kalman filter, population dynamics, seasonal forecasting, sea-
surface temperatures
Journal of Applied Ecology
, 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 Page 31 Thursday, January 17, 2002 2:44 PM
M.C. Todd
et al.
© 2002 British
Ecological Society,
Journal of Applied
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-
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 Page 32 Thursday, January 17, 2002 2:44 PM
Brown locust
outbreaks and
climate variability
© 2002 British
Ecological Society,
Journal of Applied
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
) was separated from the
high-frequency (HF) component (with periodicities
< 11 years; D
), 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
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
resolution, respectively. Monthly anomalies of
SST in the Niño-3·4 region of the central equatorial
Pacific (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
et al
. 1996). This provides monthly data on a
global grid for the period 1948–present.
The relationship between D
and low-frequency
climate variability was assessed by comparison of the
time series of D
and low-frequency rainfall variabil-
ity (R
) over the SA region. We used rainfall time
series (R
) 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
over the Karoo (grid cell centred on 32·5
S, 26·25
To assess the influence of climate on the high-
frequency component of brown locust variability, the
relationship of D
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
was cor-
related with the time series of climate variables at each
grid cell in the global field. To assess predictability of
from climate, the D
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
data were
ranked and the five most extreme years of high and
low locust activity were identified. Mean anomalies Page 33 Thursday, January 17, 2002 2:44 PM
M.C. Todd
et al.
© 2002 British
Ecological Society,
Journal of Applied
of NCEP climate data were then calculated for these
samples at each grid cell and statistical significance
was tested using a
To evaluate the predictive strength of the relation-
ship between brown locust variability and rainfall, esti-
mates of D
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
value predicted from the regression of D
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
relative to the observed D
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
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.
   
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
and D
shown in Fig. 1. Analysis of the autocorrelation func-
tion of D
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
and, although the periodicity of the two
time series was less clear, there was some
evidence of periodicity near 18 years (Fig. 3). However,
the D
cycle led both the JFM and OND R
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
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
No. districts reporting control (D)
1960 1970
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).
Period (years)
Fig. 2. Spectral density of raw time series of the number of
magisterial districts recording brown locust outbreaks.
Standard LF anomalies
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). Page 34 Thursday, January 17, 2002 2:44 PM
Brown locust
outbreaks and
climate variability
© 2002 British
Ecological Society,
Journal of Applied
Jan Feb
Jan - 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). Page 35 Thursday, January 17, 2002 2:44 PM
M.C. Todd
et al.
© 2002 British
Ecological Society,
Journal of Applied
over a broader region of SA in October. Correlations of
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 (
= 0·64) was observed between
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
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
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
198586, 1950–51, 1970–71, 196364, 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
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
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
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)
0500400300 600
Annual rainfall (mm)700 800 900
HF component of D
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.
Correction coefficient
Month (prior to locust season)
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). Page 36 Thursday, January 17, 2002 2:44 PM
Brown locust
outbreaks and
climate variability
© 2002 British
Ecological Society,
Journal of Applied
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
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
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
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). Page 37 Thursday, January 17, 2002 2:44 PM
M.C. Todd
et al.
© 2002 British
Ecological Society,
Journal of Applied
region (37·542·5
S, 11·5– 22·5
E) for the OND season
had a correlation of 0·5 with D
. Throughout the
austral early summer period there were negative corre-
lations between D
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
not be entirely ENSO related.
In December prior to high D
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
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
(data not shown).
Understanding the nature of brown locust populations
over SA so that effective control measures can be
implemented, could potentially result in considerable
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
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. Page 38 Thursday, January 17, 2002 2:44 PM
Brown locust
outbreaks and
climate variability
© 2002 British
Ecological Society,
Journal of Applied
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
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
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. Page 39 Thursday, January 17, 2002 2:44 PM
M.C. Todd et al.
© 2002 British
Ecological Society,
Journal of Applied
Ecology, 39,
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.
The authors are grateful to the UCL and the University
of Oxford for support. NCEP reanalysis data were Page 40 Thursday, January 17, 2002 2:44 PM
Brown locust
outbreaks and
climate variability
© 2002 British
Ecological Society,
Journal of Applied
Ecology, 39,
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:// 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
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Received 11 May 2001; final copy received 20 September 2001 Page 42 Thursday, January 17, 2002 2:44 PM
... The cyclical nature of outbreaks is partly influenced by general climatic patterns, particularly rainfall (Todd et al., 2002), implicit in the episodically irruptive nature of the Karoo (Milton et al., 1999). Cycles are also influenced by intrinsic factors, such as the local depletion at outbreak centres of solitaria with the genotypic propensity to aggregate (Lea, 1968). ...
... The mass triggering of hatching events and phase changes to brown locust gregaria over broad areas, though influenced by rainfall (Todd et al., 2002), also involves other factors that are not yet understood, as pointed out by Smit (1960) for one outbreak centre and Lea (1968) concerning the simultaneous irruptions in 1963 at distant locations with different rainfall. Smit's and Lea's datasets are appended in Tables S1 and S2 to be accessible for further investigation of other potential drivers. ...
Populations of brown locusts Locustana pardalina (Walk.) (Orthoptera, Acididae, Oedipodinae) alternate between resident solitaria grasshoppers in the Karoo via a transiens phase to nomadic gregaria locusts that periodically swarm across and beyond the Karoo. Concerns about crop damage led to this species being declared a pest in 1911 to be controlled with insecticides. Despite over 225 years of records of brown locust outbreak events and a considerable body of research during the early to mid-20th century, research impetus waned while outbreak events, as well as efforts at, and financial and ecological costs of chemical control, have steadily escalated. This review highlights particularly insightful field observations made by scientists between the 1920s and 1960s, which have yet to be followed up with further research. We revivify knowledge of brown locust solitaria ecology, including their diet, mainly consisting of the short grass, Enneapogon desvauxii, the cumulative build-up of egg banks with quiescent embryos, and how five to seven successive generations build up densities until crowding of nymphs brings about incipient outbreaks of gregaria locusts, which can aggregate into large swarms that depart to remote locations. Surprisingly, no quantitative records exist of the potential negative impacts at large scales of brown locusts on rangeland grazing or crop yields, nor have their potentially important roles for Karoo ecosystem functioning been well-documented. Although the quality of rangeland management affects the dynamics of outbreak centres, this recognition has not been followed up with experiments and detailed observations to make definite recommendations on farming practices. We suggest several avenues of research that build on the existing knowledge with modern techniques and fill the most important knowledge gaps to improve managing brown locust populations sustainably.
... They also report all invasions to the municipality and onwards to the province administration for further support and preparedness. Insights from South Africa [12,43], show similar patterns, with empowerment of local institutions being key for the successful control and management of locust invasions. Empowerment of local entities facilitates ground truthing, which can facilitate the availability of up-to-date information and improved knowledge about the ecology of the pest, as well as timely and consistent monitoring and evaluation, all of which have been shown to be important for the preventative management of locusts [44]. ...
... The national level institute in China is not involved in early warning and preparedness activities. This might be worth considering going forward, as evidence from South Africa shows that national government involvement in early warning and preparedness actions and activities has been instrumental in the prevention of brown locust plagues in South Africa [43]. ...
Full-text available
Using qualitative methods, this study assessed the stakeholders and management processes involved in locust outbreaks in China, including factors influencing the use of biopesticides. Study findings show that China has an integrated national locust response protocol, which involves various institutions from all administrative levels of the government. The process is inherently highly complex but efficient, with multisectoral agencies working closely together to prevent and/or manage locust outbreaks. In addition, the process has been successful in combating recent outbreaks, due to dedicated government funding, decisive administrative and technical actions, and the empowerment of local government administration. This is the case with the county level acting as a ‘first responder’ that is capacitated financially and technically to respond to a locust invasion in their jurisdiction. Additionally, study findings show that despite the availability of biopesticides in local markets, their use is dampened by inadequate information about market availability, negative perceptions by decision-makers about their efficacy, and concerns about their costs, as well as limited knowledge of their application techniques. Actions are therefore needed by relevant authorities to enhance stakeholder awareness of biopesticide market availability, efficacy, and field application processes. Future areas of research should focus on modelling the expected impact and cost-effectiveness of chemicals vs. biopesticides, thus increasing the evidence base for promoting biopesticide use.
... Nevertheless, the impact of long-distance dispersal (such as locust swarms) in grasshopper community assembly cannot be ruled out based on these observations alone. The propensity for some orthopteran species to swarm(Todd et al., 2002) and/or emerge on mass(van der Mescht et al., 2022) means that recent dispersals may cause species to dominate landscapes only temporarily. Longer-term data, coupled with a more intensive analysis of functional diversity, will be needed to resolve whether grasshopper communities in plains and mountains are regulated around one or several stable states. ...
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South Africa is a megadiverse country. Here, natural communities are unevenly distributed across, and within, seven distinct biomes. In such heterogeneous landscapes, understanding spatial patterns of biodiversity is essential for planning and implementing efficient conservation measures. The southern Kalahari, forming part of South Africa's savanna biome, is an arid region of peculiarly high diversity and endemism. The responses of orthopteran assemblages to changing environmental conditions across the Kalahari were investigated by comparing alpha and beta diversity levels across discrete vegetation types in the Tswalu Kalahari Reserve. The degree of association between species and specific vegetation types were also studied and how a key life history trait ‐ dispersal ability – influences community composition was determined. This study identified 46 grasshopper species within the reserve, which compares well with richness levels in other more productive habitats of the country. Local (alpha) diversity was higher in mountain and mountain‐ecotone sites versus vegetation types on the plains, and species turnover was also exceptionally high – approaching 100% ‐ across these two groups. The few (3) dispersal limited species recovered were associated only with the mountain‐ecotone group, with emergent dominance patterns suggesting that competitive rather than dispersal abilities determine the species composition of unique assemblages in the landscape. Topology plays a key role in maintaining spatial diversity across the southern Kalahari landscape. Mountains, and their ecotones, promote not only species turnover, but also richness and functional diversity. These can be viewed as islands of diversity, and should be targeted priority areas for conservation beyond the boundaries of protected areas.
... These included 19 butterfly species in Canada and the USA (Vandenbosch, 2003;Harrison et al., 2015;Pardikes et al., 2015), two fruit fly species in Mexico (Aluja et al., 2012), 67 social wasp species in French Guiana (Dejean et al., 2011), one moth species in Argentina (Paritsis & Veblen, 2011), locusts in China (Zhang & Li, 1999), and two planthoppers in Japan (Morishita, 1992) (Table S1). Only eight species showed a significant association with ENSO − (Table 1): hessian fly and three butterfly species in the USA (Woli et al., 2014;Harrison et al., 2015), social wasps in Chile (Estay & Lima, 2010), locusts in South Africa, Botswana and Namibia (Todd et al., 2002), and two moth species in Australia (Maelzer & Zalucki, 2000) (Table S1). Dual associations with ENSO +/− were found only for three butterfly species in the USA (Harrison et al., 2015) (Table 1, Table S1), which was not significantly different from that expected by chance (G = 0.81, df = 1, p = 0.37). ...
Climate is a major extrinsic factor affecting the population dynamics of many organisms. The Broad-Scale Climate Hypothesis (BSCH) was proposed by Elton to explain the large-scale synchronous population cycles of animals, but the extent of support and whether it differs among taxa and geographical regions is unclear. We reviewed publications examining the relationship between the population dynamics of multiple taxa worldwide and the two most commonly used broad-scale climate indices, El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Our review and synthesis (based on 561 species from 221 papers) reveals that population changes of mammals , birds and insects are strongly affected by major oceanic shifts or irregular oceanic changes, particularly in ENSO-and NAO-influenced regions (Pacific and Atlantic, respectively), providing clear evidence supporting Elton's BSCH. Mammal and insect populations tended to increase during positive ENSO phases. Bird populations tended to increase in positive NAO phases. Some species showed dual associations with both positive and negative phases of the same climate index (ENSO or NAO). These findings indicate that some taxa or regions are more or less vulnerable to climate fluctuations and that some geographical areas show multiple weather effects related to ENSO or NAO phases. Beyond confirming that animal populations are influenced by broad-scale climate variation, we document extensive patterns of variation among taxa and observe that the direct biotic and abiotic mechanisms for these broad-scale climate factors affecting animal populations are very poorly understood. A practical implication of our research is that changes in ENSO or NAO can be used as early signals for pest management and wildlife conservation. We advocate integrative studies at both broad and local scales to unravel the omnipresent effects of climate on animal populations to help address the challenge of conserving biodiversity in this era of accelerated climate change.
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Like any other edible insects, locusts are an alternative source of protein that could supplement livestock and human foods. This study selected indigenous locust species with a high reproductive and rapid growth rate to determine the most favourable feeding plant species for locust mass-rearing. A suite of seven locust species, Acanthacris ruficornis (Fabricius, 1787), Cantantops melanostictus (Schaum, 1870) Locusta migratoria (Linnaeus, 1758), Petamella prosternalis (Karny, 1907), Chortoicetes terminifera (Walker, 1870), Cataloipus zulvensis (Sjöstedt, 1929) and Ornithacris cyanea (Uvarov, 1924) (Orthoptera: Acrididae) were collected from tribal orchards in Lufule, Tshikweta and Belleview villages in Limpopo province, South Africa and reared on the crop feeding plant species. The feeding, reproductive output (nymph production), adult longevity, and mortality of the suite of the locust species were determined on Zea mays L. (Poaceae), Phaseolus vulgaris L. (Fabaceae) and Panicum maximum jacq. (Poaceae) under the control, choice-one, choice-two, and choice-three tests. Choice-one and -two tests had three and two feeding plants, respectively. Whilst both the control and choice-three tests had a single-feeding plant species. I found that locust feeding, reproductive output, adult longevity, and mortality depended on the plant species, and this was more significant for the control, and choice-one compared to the choice-two and choice-three tests. Both the nymphs and adults of the locusts fed significantly more on P. vulgaris and Z. mays, respectively. Locust colonies exposed to P. maximum in the choice-three test reproduced and survived lesser significantly than all the tests. Results suggest that the combination of P. vulgaris and Z. mays or P. vulgaris alone can be used to mass-rear the suite of the locust species, particularly C. melanostictus, A. ruficornis and C. terminifera. Presented here are the most sustainable locust-rearing methods using crop plant species with rapid propagation responses. These results could be implemented as either extensive-or small-scale rearing for research or commercial purposes in South Africa and elsewhere.
A study was conducted in August 2011 in the Lower Shire Valley districts of Chikwawa (Ntombosola village, TA Chapananga) and Nsanje (Mlolo village, TA Mlolo, and Nyachikadza village, TA Nyachikadza) to document and scientifically validate the most commonly used naturally occurring traditional early warning signals for floods and drought, which are the major hazards threatening the resilience of social and ecological systems in the area. The data collection approach included desk study (literature search), a field survey using standard participatory approaches (Focus Group Discussions and Key Informant Interviews) through which data and information on meteorological, hydrological, socio-economic and ecological data for the study areas was collected and analysed thematically. The study identified and analysed 22 traditional early warning signals for rainfall, floods and drought as observed and reported by communities in the study areas out of which, eight are directly associated with droughts while three are associated with floods. The remaining 11 neither predict drought nor floods but act as early warning signals for seasonal changes and weather. Two flood-related traditional early warning signals (EWS), namely, higher distribution of hippos and snails in human environment, exhibit some consistency with scientific knowledge, but the third, mushroom abundance, is off target. However, all drought-related traditional EWS have limited consistency with scientific knowledge (SK), which means that they do not meet the minimum criteria to act as early warning signals of drought or floods. In addition, all seasonal/weather-related signals also have limited consistency with SK. In particular, eclipse of the moon is totally inconsistent with SK as an EWS. These inconsistencies are a manifestation of the existing gap between scientific knowledge and indigenous knowledge on the early warning signals of drought and floods in the area. The predictions by the traditional EWS for drought and floods have not always resulted in the specific episode as predicted rendering them unreliable. Reliance on scarce wildlife such as pangolins, foxes, hippos and pythons raises further concerns that communities may not easily access these indicators when needed. Since this knowledge has been passed on orally from one generation to the next, there is high possibility of distortion over time, as is often the case with oral history. Climate change and climate variability have exacerbated the inconsistencies. These results provide the framework on which disaster risk reduction interventions may be developed particularly as regards behavioural change communication around community-based early warning disaster risk reduction. Using this information, the Government of Malawi and stakeholders may identify the key messages and strategies for addressing the knowledge gaps. It is recommended that detailed studies be conducted to ascertain change in abundance of snails and the behaviour of hippos in relation to floods in the Lower Shire. Since most of the traditional EWS have limited consistency with SK, we recommend that the information should be enhanced with SK, refined and repackaged for use by relevant communities. For example, farmers would be informed that drought conditions favour locust outbreaks, while elegant grasshoppers boom with water-stress conditions within the rainy season. This would prepare the farmers for looming locust disasters to take precautionary risk reduction measures.
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Locusts are a threat to agriculture and livelihoods in many countries globally. The economic, social, and environmental consequences of these highly migratory pests are so substantial that they are treated as a national priority by many countries and several international commissions have been established to unite efforts. This book, a special issue of the Agronomy journal, aims to shed light on some overarching questions: What have we learned from historical outbreaks, how serious is the threat, what research is ongoing and is needed to better manage these insects, how should the world respond to plagues today especially in the context of climate change, are recommended preventive strategies really effective and what are the constraints to their application, and is there a possibility to make better use of biological alternatives to chemical pesticides? This book is freely accessible on the MDPI Books platform:
Grasshoppers are preeminent herbivores and perhaps the most significant rangeland pests in the United States (US). Despite the important ecosystem functions they provide, grasshopper populations often obtain densities that cause significant economic harm to grazing operations and agricultural production. Although numerous studies conducted at the level of individual field sites have examined potential mechanisms contributing to grasshopper population “boom and bust” cycles, there has yet to be a large, regional scaled analysis that quantified grasshopper variation across the Western US as a whole. While taking steps to account for data collection biases, mediating effects, and variable confounding, we assessed the influence of Pacific Ocean sea surface temperature oscillations on a 40-year record of grasshopper density in the Western US. Central to our analysis was employing spatially varying coefficients to model time and location-specific variation in grasshopper response to climate. Our results quantitatively demonstrated interannual changes in grasshopper density to be indirectly effected by seasonal El Niño/Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) variability and to exhibit spatial asynchrony and non-stationarity such that the relative influence of climate on grasshopper density varied through time and across geographic space. Our model is the first to incorporate climate indices as spatially varying coefficients for assessment of a terrestrial species and represents a critical step towards understanding causal drivers of regional grasshopper density.
The Indian Ocean Dipole (IOD) is a basin-wide ocean-atmosphere phenomenon that has profound impacts on the global climate, land, and ocean. The Dipole Mode Index (DMI), which is defined as the difference of the SST anomaly in the east IOD zone and the west IOD zone has long been used to characterize and quantify the strength of IOD events. In this study, we propose biological dipole mode indices (BDMIs) based on the dipolar observations of chlorophyll-a (Chl-a) anomalies (difference and relative difference) in the east and west IOD zones during the IOD event. The two BDMIs, which are based on Chl-a difference and relative difference, not only represent the dipolar biological activities in the Equatorial Indian Ocean, but also reflect the thermocline dynamics in the east IOD zone and west IOD zone. The in situ measurements in the east and west IOD zones show the clear linkage between the BDMIs and the dynamics of the 20°C isothermal depth. This linkage is attributed to the changes of the nutrient supplies driven by the various ocean physical processes in the IOD event, thus the BDMIs could also act as the surrogate for the thermocline dynamics in the two IOD zones. The BDMIs from satellite ocean color observations show that they can identify and characterize all the major IOD events in the last two and half decades. The SST-based DMI and Chl-a-based BDMIs may depict some different aspects of the IOD events (e.g., surface versus subsurface properties). The performance comparison between the two BDMIs and DMI also shows that the BDMIs and traditional DMI can effectively detect IOD signal for the major IOD events. Indeed, the BDMI and DMI indices are complementary for characterizing the IOD events, and the combination of these indices can provide a better understanding of the atmosphere and ocean processes for both surface and subsurface, as well as biological processes in the Equatorial Indian Ocean.
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Meeting the energy demands and sustainable development goals in Nigeria requires investigation of potentials of alternative energy sources and possible challenges to their reliability. In this study, we investigated the impact of four (4) teleconnection patterns on the solar energy potential within different climatic zones of Nigeria. Our results indicate that there are weak and insignificant correlation between the studied teleconnection patterns and solar energy potential on the long run. However, monthly analysis suggests significant correlation values between all the teleconnection patterns studied and solar energy production within all the climatic regions of the country. Therefore, it is important to consider the role of teleconnection pattern in energy planning and forecasting within the region.
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There is increasing evidence of coherent patterns of variability on near quasi-bidecadal time scales in a range of climatic data from many parts of the world. Folland et al. (1984) found peaks at periods of 16 and 21 years respectively in spectra of globally- averaged sea surface temperature (SST) and night marine air temperature (NMAT) for 1856–1981. Newell et al. (1989) found variations near a period of 21 years in global and Southern Hemisphere NMAT for 1856–1986, and to a lesser extent in Northern Hemisphere NMAT and global and hemispheric SST. Ghil and Vautard (1991) drew attention to variations on approximately 20-year time-scales in globally-averaged anomalies of combined land surface air temperature and SST for 1854–1988, though Allen and Smith (1996) question the statistical significance of their results. Mann and Park (1994) found a 15–18 year mode in fields of mainly land surface air temperature anomalies for 1891–1990. They suggested that this mode, which had a pattern similar to that of the thermal signature of the interannual El Niño-Southern Oscillation (ENSO), may be a manifestation of long timescale modulation of ENSO as well as being the reason for Ghil and Vautard’s (1991) global-average result. Latif and Barnett (1996) discussed near bidecadal variations in SST and atmospheric circulation over the North Pacific in both observations and a coupled model, and the consequential variations of temperature and precipitation over North America. In the Southern Hemisphere, Venegas et al. (1996) found a coupled mode in South Atlantic SST and mean sea level pressure (MSLP) data for 1953–1992, with significant variations on near 15-year timescales and provided evidence that the atmosphere was forcing the ocean.
The succulent and Nama-karoo form part of the arid south-western zone of Africa, a vast region of rugged landscapes and low treeless vegetation. Studies of this unique biome have yielded fascinating insights into the ecology of its flora and fauna. This book, originally published in 1999, is the first to synthesise these studies, presenting information on biogeographic patterns and life processes, form and function of animals and plants, foraging ecology, landscape-level dynamics and anthropogenic influences. Detailed analyses of the factors distinguishing the biota of the Karoo from that of other temperate deserts are given and generalisations about semi-arid ecosystems challenged. The ideas expounded, the ecological principles reviewed, and the results presented are relevant to all those working in the extensive arid and semi-arid regions of the world.
The model tested the hypothesis that locust swarming follows the summer rainfall that ends extended dry periods, and is inhibited by winter rainfall. This hypothesis was partly confirmed by comparing rainfall data and swarming activity at 12 stations situated within the locust outbreak area from 1910-1986. -from Authors
The main approaches, assumptions and methods used in seasonal forecasting are described in this paper. Examples of methods ranging from simple correlation to multi-annual, multivariate models of seasonal rainfall prediction are given for several regions in Africa along with an overview of operation forecasts for the Sahel, East Africa and Southern Africa. Recent developments in climate prediction suggest that seasonal rainfall forecasts for Africa are increasingly reliable and should be of widespread interest to resource managers and consumers. Climate forecasts may indeed revolutionize resource management in Africa. Yet, their utility depends on the linkages between geophysical, economic and social aspects of resource use. Progress in rainfall forecasting is placed in the context of the use of seasonal predictions in Africa, with a particular emphasis on ameliorating vulnerable livelihoods. Targeting users, reaching vulnerable livelihoods, messages that are distorted over space and time, the lag between forecasts and dissemination, maladaptive responses and false alarms are difficulties that can be expected in many developing countries.