African Crop Science Journal African Crop Science Society ISSN: 1021-9730 EISSN: 2072-6589 Vol. 8, Num. 3, 2000, pp. 337-343

African Crop Science Journal, Vol. 8. No. 3, pp. 337-343

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DISPERSAL, PHENOLOGY AND PREDICTED ABUNDANCE OF THE LARGER GRAIN BORER IN DIFFERENT ENVIRONMENTS

G. FARRELL
Natural Resources Institute, University of Greenwich, Chatham Maritime, Kent ME4 4TB, UK

(Received 13 December, 1999; accepted 27 August, 2000)

Code Number: CS00036

INTRODUCTION

The larger grain borer (LGB)(Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae)) is a serious pest of stored maize and cassava that was introduced into Tanzania in the early 1980s (Dunstan and Magazini, 1981). The international grain trade, normal beetle flight activity and the pest’s ability to survive and breed outside the storage environment have limited the success of control campaigns. These survival mechanisms made it likely that the pest would continue to spread in Africa, wherever agro-climatic conditions and food sources were favourable. Despite concerted efforts by national programmes and international agencies, using quarantine, chemical and biological control initiatives that have slowed the rate of dispersal of LGB, its advance in Africa has been relentless (Hodges, 1986).

Interest in the potential range of LGB in Africa prompted several investigations of the pest’s phenology and dispersal, based on trapping data from West and East Africa. In Kenya, the temporal and spatial distributions of the beetle have been investigated to determine its rate of spread to uninfested areas, through normal flight activity and unwitting human intervention, and to measure flight response of the beetle to aggregation pheromones (Giles et al., 1995; Nang’ayo, 1996). Similar investigations have been carried out in other countries, which may provide insights to the ultimate distribution of LGB in Africa. For example, Tigar et al. (1994a) put forward a model for LGB abundance from a study of trap catches, climatic and habitat factors in Mexico, whereas Haubruge and Gaspar (1990) predicted LGB distribution based on laboratory studies.

This paper summarises recent work on larger grain borer attraction, dispersal and phenology, compares two different predictive models of LGB numbers in Africa with laboratory studies, and discusses the value of such forecasts in identifying countries or regions at risk from the pest.

Attraction and dispersal. Adult LGB are attracted to maize grains (Detmers, 1990; Wright et al., 1993) and dried cassava (Wright et al., 1993) over short distances. Pike et al. (1994) extracted volatiles from maize and cassava that attracted LGB in laboratory tests, whereas Tigar et al. (1994a), suggested there was no long distance attraction to maize grain or cobs. In addition, Wright et al. (1993) in Togo found no long-range attraction to dried cassava or maize. However, failure to arrive at these food sources is unlikely to be due to any inadequacy of flight ability, as under laboratory conditions Pike (1993) found that beetles attached to a flight mill could travel 25 km in 45 h, showing that LGB is a fairly strong flyer. There is no evidence that flight periods in the natural environment last as long or involve such distances.

Development of successful pheromone-baited traps for the detection of LGB involved investigations of the range of attraction of the aggregation pheromone. Farrell and Key (1992), in central Mexico, used a mark-release-recapture technique to show, over 24 h, that LGB would fly in a directed way to a pheromone source at an upwind distance of 50-100 m from the release point. The maximum release point-to-trap distance at which beetles were caught was 340 m, though it could not be shown that the insects were responding to the pheromone at this distance, since beetle flight direction and wind direction were not correlated. Rees et al. (1990a), working in Yucatan, used a similar method and measured dispersal over 250 m in 72 h. In Honduras, Novillo (1991) gave a dispersal distance in 48 h of over 300 m. It is likely that energy limitations encourage the beetle to fly downwind until pheromone is detected. Flight is then directed upwind to the pheromone source. More recent investigations by Hodges et al. (1998) suggest that different components of the pheromone plume elicit different behavioural responses, in that the T2 component is a long-range attractant, and the T1 component modified the response to T2, to facilitate close-range orientation.

LGB substrate-finding behaviour seems to occur through dispersal of beetles from a deficient food or breeding site, by following a pheromone plume or during random flight activity. On arrival, test burrows are made in search of food (Hodges, 1994). If the substrate is not suitable, the beetle flies off in search of a better supply, but a suitable food source induces the production of the aggregation pheromone by the male, which attracts both males and females to the site. Hodges (1994) further concluded that the male stops producing the pheromone when joined by a female.

The relatively short distances over which the pheromone elicits LGB flight would not be sufficient to explain the spread of the pest over large areas of Africa in the last decade. From its first African report in Tanzania in 1980, the pest has since spread to central and southern Africa and to West Africa (Table 1), though the outbreak there is thought to result from a separate introduction from outside Africa.

The slow rate of LGB spread through normal flight activity suggests that the maize trade has been responsible for its wide occurrence, particularly when one considers that new outbreaks within East and southern Africa have been mainly reported near road and rail trade routes. There is considerable intra-regional maize movement, through commerce and aid shipments, which could have contributed to the distribution of LGB in the region, though some success in slowing the rate of spread using phytosanitary measures has been achieved (Schulten, 1996).

LGB phenology and flight activity based on trap catches. Pheromone-baited sticky traps have been extensively used to study LGB activity in the natural environment. Large catches have been made in woodland in Mexico (Rees et al., 1990b), and in Tsavo National Park in Kenya (Nang’ayo et al., 1993). Nang’ayo et al. (1993) tested dry twigs and found 16 tree species in Kenya that supported breeding of LGB, and suggested that climate effects could explain seasonal dynamics of LGB. There seems to be no shortage of suitable wood as breeding material in sub-Saharan Africa, though lack of dry wood in wet areas may be a limiting factor during parts of the year.

In Honduras, Novillo (1991) found a daily bimodal pattern of flight activity, with major catches at dawn and a lesser peak at dusk. Tigar et al. (1993) in Mexico and Giles et al. (1995) in Kenya revealed similar patterns but the time of peak catches were reversed, compared to Honduras. Tigar et al. (1993) suggested pheromone production may be limited to dawn and dusk periods, to conserve energy and reduce exposure to predators, such as T. nigrescens, that also respond to the pheromone. However, wind tunnel work by Fadamiro et al. (1996) indicated that beetles at rest do not take off in the presence of pheromone. It seems that the pheromone elicits a response only from beetles already in the air. The mediation mechanism that controls production of and response to the pheromone has not been described.

Trapping over extended periods has shown marked seasonal variations in Honduras (Novillo, 1991), Kenya (Nang’ayo et al., 1993) and Togo (Wright et al., 1993). These variations are probably related mainly to climatic conditions, and a lesser extent to vegetation such as the presence of maize or forest near the traps (Tigar et al., 1994b). However, this assumes that fluctuations in trap catch represent changes in the base population, and not just differences in numbers of LGB that respond to pheromone. In Kenya, catch maxima seem to occur just before the onset of the rains, and may reflect the build up of the population in the preceding dry period.

Predicting LGB abundance in different agro-climatic zones. Haubruge and Gaspar (1990) produced maps of predicted LGB distribution in Africa, derived from laboratory studies. They showed that LGB survived best at temperatures around 30°C, with relative humidities above 60%. Shires (1979) obtained similar results from laboratory work, and reported optimal LGB growth at 32°C and 80% RH. Hot and humid areas should therefore have the highest numbers of LGB. This interpretation was disputed by Tigar et al. (1994b), who presented a model for predicting LGB abundance in East Africa based on trap catches and climatic factors in Mexico. In their model, the abundance of LGB (the number of beetles caught every two weeks) varied with the mean annual per cent relative humidity (measured at 09.00h) and temperature, and the total annual rainfall (thus the LGB abundance value can only be estimated on a yearly basis). The optima were 23-25°C and 50-52% RH. This model predicted higher LGB numbers in less hot, but drier areas, such as are found at higher altitudes. Giles et al. (1995) produced a simpler model, also based on climatic factors, from data collected at Kiboko (in an area with low rainfall and mean annual temperatures of 23.9°C, 160km south-east of Nairobi). The model of Giles et al. can be used to estimate monthly fluctuations in LGB numbers.

Tigar et al. (1994b) and Giles et al. (1995) analysed data as multiple linear models in regression equations linking LGB numbers to rainfall, temperature and relative humidity.

Tigar et al. (1994b) log_ep = 0.00063r + 0.57t - 0.01t² + 0.63h - 0.0061h² - 19.74

Giles et al. (1995) MP = 1.39R + 0.781T + 0.283H - 33.79

(where r = annual rainfall; t = mean annual temperature; h = mean annual relative humidity recorded at 09.00h; p = LGB catch/year; MP =LGB catch/month; R = mean daily rainfall; T = mean daily temperature; H = mean daily relative humidity)

Tigar et al. (1994b) also assessed habitat effects (crop, tree species and land use) but only the area of maize was significant. This variable was not relevant when traps were sited in the natural environment away from maize fields.

DISCUSSION

The model of Tigar et al. differs considerably in its predictions from that of Haubruge and Gaspar, but seems to reflect actual LGB abundance, at least in Mexico. Laboratory studies use insects of limited genetic base, isolated from fluctuations in climate, on a favoured substrate (maize) and lacking pathogens, predators and competitors, all of which can have serious effects on insect numbers under more dynamic conditions. However, Tigar et al. (1994b) cautioned that it would be unwise to expect their model to predict the numbers of LGB likely to be trapped in East Africa, and this is borne out by current trap data from Kenya. Using information from three sites in Kenya (Mombasa [hot and humid], Kiboko [less hot and dry] and Wundanyi [cool and humid]) the predicted LGB abundance should not vary greatly. But as Table 2 shows, actual catches do differ markedly. It is noteworthy, however, that the model of Tigar et al. correctly predicted that hot dry areas would have higher catches than cool wet or hot wet locations, though the size of the catch was not accurately forecast.

Changes in relative humidity have the most effect on the predicted abundance. Thus, at Kiboko the predicted catch can only approach the actual catch if the relative humidity is about 50%. Changing the temperature and rainfall parameters has comparatively little effect on predicted LGB numbers. The model of Tigar et al., therefore, does not discriminate sufficiently at these climatic ranges and altitudes. It is only when the relative humidity is near 50% at 2500m and above, for example on the middle slopes of Mt Kenya and Mt Kilimanjaro, that major site differences in LGB abundance would become apparent.

Assuming measurements of meteorological parameters were accurate in Mexico and Kenya (relative humidity in particular can vary widely during the day) there could be several reasons for the discrepancy between predicted and actual catches. These may include the absence of the main predator associated with LGB in central America (Teretrius (=Teretriosoma) nigrescens (Lewis) Coleoptera: Histeridae)) and other biological antagonists that did not arrive at the same time as the pest, the nature of the population (spreading in East Africa but presumably stable in Central America) or different and more attractive food sources in Africa.

If the predator is effective in reducing LGB numbers then the model may become a more accurate reflection of field catches in East Africa because it was derived from ecosystems in which the predator was active. However, the main use of such models may lie in predicting relative, rather than absolute, LGB abundance. Areas at risk identified by the model could be targeted for pre-emptive awareness campaigns, though it is still not clear what relationship exists, if any, between abundance of LGB in the natural environment and the severity and extent of store infestations.

ACKNOWLEDGEMENTS

Production of this paper was supported by the British aid programme to Kenya, under the KARI/DFID National Agricultural Research Project, Phase II. Mr J. A. Sutherland provided valuable comments on the early draft. Any views and opinions expressed are those of the author and do not necessarily represent the opinions of the Kenya Agricultural Research Institute or the Department for International Development.

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