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- Materials and methods
It is now widely accepted that the climate is changing as a result of ‘greenhouse gases’ produced by human activity. Predictions for warming vary, but most range from 1·5 °C to 5 °C by 2080 (Hulme et al. 2002). Changes to precipitation patterns are also likely, but are more difficult to predict with accuracy, although most models suggest that winter rainfall will increase in northern temperate regions (Houghton et al. 1996; Hulme et al. 2002). These changes are expected to have profound effects on species richness; recent studies predict that up to 37% of organisms may be ‘committed to extinction’ by 2050 because of climate change (Thomas et al. 2004). Major consequences for agricultural production (Parry 1992; Rosenzweig & Hillel 1998; Kriticos et al. 2003), forestry (Schwartz 1992), plant community composition (Buckland et al. 2001), the incidence of insect pest outbreaks (Sutherst 1995; Cannon 1998; Tenow et al. 1999) and of diseases transmitted by insects (Langford & Bentham 1995; Lindsay & Birley 1996; Sutherst 1998) are also expected. Attention has focused particularly on butterflies and birds, two groups for which extensive distributional and abundance data are available. For both groups specialized, sedentary species and those with a narrow latitudinal range appear to be most at risk of extinction as the climate warms (Roy et al. 2001; Bryant, Thomas & Bale 2002; Crick 2004; Julliard, Jiguet & Couvet 2004; Thomas et al. 2004). However, vagile generalists with broad latitudinal tolerance are likely to cope better, and thermophilic species (including many insect pests) may increase in abundance (Sutherst 1995; Cannon 1998; Baker et al. 2000).
Many calyptrate flies (Diptera: Cyclorrhapha: Calyptratae), particularly species of the Muscidae, are thermophilic, vagile and have large latitudinal ranges (Crosskey & Lane 1993). The propensity of the adults of many species to feed on human food, as well as refuse and excrement, means that they are important vectors of human disease (Crosskey & Lane 1993). Some calyptrates also cause myiasis in humans and livestock (Hall & Smith 1993), and are intermediate hosts of parasitic nematodes attacking livestock (Snow 1974). The annoyance and public health risks associated with large populations of such flies are thus considerable, and potential increases in their abundance as a result of climate change are a cause for concern.
Human populations produce considerable quantities of waste, with a large organic component suitable as a breeding site for many calyptrate fly species (Schoof, Mail & Savage 1954; Siverly & Schoof 1955a,b; Wilton 1961; Ikeda et al. 1972; Dirlbek 1986; Ferriera & Lacerda 1993; Werner 1997). Up to 10 million flies can emerge from just 1 ha of household waste (Abdel-Gawaad & Stein 1978). In Japan, Imai (1985) found that 1300–1500 flies emerged m−2 of landfill surface in 1 month following deposition. The abundance of food provided by the regular supply of organic waste, combined with above-ambient temperatures present immediately below the surface layers, helps promote the rapid proliferation of many fly populations throughout much of the year (Goulson, Hughes & Chapman 1999; Howard 2001). Species that are particularly likely to be problematic in terms of human health include the housefly Musca domestica Linnaeus (Muscidae) and bluebottles (Calliphora spp., Calliphoridae). These are synanthropic species, living in close association with humans. Musca larvae will develop in a wide range of decomposing organic material, while those of Calliphora feed primarily on animal matter (Colyer & Hammond 1968). However, there are many other species of pestiferous medium to large flies that breed within the organic component of human waste (Dirlbek 1986; Ferriera & Lacerda 1993).
A notable feature of fly populations, particularly those of M. domestica, is their propensity for very rapid fluctuations in population density, often with adult numbers increasing by up to two orders of magnitude in a few days (Imai 1984; Essa & El Sibae 1993; Goulson, Hughes & Chapman 1999). These population outbreaks are a major cause for concern because high density populations have a high nuisance value and may cause local outbreaks of disease such as gastro-enteritis.
We used a 4-year data set of weekly fly records from southern England to examine whether numbers of M. domestica, Calliphora spp. and all calyptrate flies can be predicted from meteorological data. We use models derived from this data set to predict numbers of flies under the warmer climatic conditions that may be expected in the future.
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- Materials and methods
In total 102 890 calyptrate flies were recorded, of which 19 914 were M. domestica and 6031 were Calliphora spp. A great many other calyptrate fly species were also captured, although most were relatively rare (Table 2). Numbers varied enormously with season (Fig. 1), with few flies caught between November and April and large numbers during the summer months. It was also noticeable that 2000 and 2001 had higher fly numbers in the summer than 2002 and 2003, with some very high peaks of up to 200 flies trap−1 (Fig. 1). These were in part attributable to the M. domestica population.
Table 2. Species composition and abundance of synanthropic Diptera and a list of other abundant calyptrate species caught on sticky traps on landfill sites in southern England
|Species||% of total catch||Mean catch per trap per week|
|Housefly (Musca domestica)||17·4||2·50|
|Bluebottle (Calliphora vomitoria+Calliphora vicinia)|| 9·00||1·29|
|Greenbottle (Lucilia spp.)|| 5·23||0·75|
|Face fly (Musca autumnalis)|| 0·3||0·04|
|Lesser housefly (Fannia spp.)|| 0·7||0·10|
|Flesh fly (Sarcophaga spp.)|| 1·9||0·27|
|Cluster fly (Pollenia rudis+Pollenia amentaria)|| 1·6||0·23|
|Other calyptrate Diptera||63·9||9·16|
|Polietes lardaria (Fabricius)|
|Neomyia cyanella (Meigen)|
|Mesembrina meridiana (Linnaeus)|
|Graphomyia maculata (Scopoli)|
|Scathophaga stercoraria Linnaeus|
Figure 1. Mean numbers of M. domestica, Calliphora spp. and total calyptrate flies caught per trap per week for the 4 years of trapping.
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Populations of M. domestica, Calliphora spp. and all calyptrate flies combined were correlated in all pairwise comparisons between sites. Pearson product–moment correlation coefficients varied from 0·404 to 0·906 for M. domestica, from 0·451 to 0·852 for Calliphora spp. and from 0·463 to 0·640 for all calyptrate fly species (P < 0·001 for all comparisons; Table 3). Traps positioned on the perimeter of active landfills were within 10 m of areas subjected to insecticidal sprays during the summer. However, comparisons between traps situated on the perimeter of the Paulsgrove landfill with those in scrubland 100–200 m distant, and in an urban area 500–700 m from the landfill, showed a close correlation in fly numbers (Table 4). Henceforth, all results are for mean catch per trap across all sites.
Table 3. Pearson product–moment correlation coefficients between total pestiferous fly catches at different sites (n = 199). All are significant at P < 0·001
| ||Paulsgrove landfill||Paulsgrove||Port Solent||Gosport||Lymington||Ringwood|
|Paulsgrove landfill||*|| || || || || |
|Paulsgrove||0·640||*|| || || || |
|Port Solent||0·463||0·532||*|| || || |
|Gosport||0·487||0·581||0·495||*|| || |
Table 4. Pearson product–moment correlation coefficients between fly catches at the Paulsgrove landfill and sites 100–200 m and 500–700 m distant from the landfill (n = 199)
| || ||Correlation|
|100–200 m from landfill||M. domestica||0·778***|
|All calyptrate flies||0·640***|
|500–700 m from landfill||M. domestica||0·756***|
|All calyptrate flies||0·463***|
Catches of all three fly groups were significantly positively correlated with catch in the previous week (Table 5). Weather variables also explained a significant proportion of the variation in numbers of all three fly groups examined during 2000–02 (Table 5). Although weather data for 4 weeks up to the fly trap collection were included in the analysis, the weather in the first of these 4 weeks had no significant effect in any of the three models. Temperature was the most powerful predictor for all three fly groups, with a generally positive correlation between fly catch and temperature in the 3 preceding weeks. For M. domestica, temperature in the week up to trap collection was the best single predictor, although other aspects of the temperature in the preceding 3 weeks independently contributed significant predictive power to the model. For M. domestica, neither rainfall nor humidity had any significant effects.
Table 5. Significant contributory factors explaining numbers of M. domestica, Calliphora spp. and all calyptrate flies during the first 3 (a, b) or all 4 (c) years of sampling. Unless otherwise stated, weather variables are weekly means. t, week up to trap collection; t − 1, preceding week, etc. ‘Fly count’ refers to the mean catch per trap of the relevant fly taxa in the previous week. r2 values indicate the proportion of the observed variation explained by the minimum adequate model shown. (a) Model includes fly count in previous week; (b) model without inclusion of fly count for previous week; (c) model with full data set to predict effects of climate change
|(a) Covariate||M. domestica||Calliphora spp.||All calyptrate flies|
|F(d.f.=1,142)||Parameter estimates ± SE||Partial r2||F(d.f.=1,144)||Parameter estimates ± SE||Partial r2||F(d.f.=1,144)||Parameter estimates ± SE||Partial r2|
|Fly countt − 1||64·8***|| 0·555 ± 0·069||0·313||141***|| 0·708 ± 0·060||0·495||52·2***|| 0·482 ± 0·067||0·266|
|Temperaturet|| || || || || || ||41·9***|| 0·051 ± 0·008||0·225|
|(Temperaturet)2||15·5***|| 0·012 ± 0·003||0·099|| || || || || || |
|Raint|| || || || || || || 4·2*||−0·012 ± 0·006||0·028|
|Humidityt|| || || || || || || 4·59*||−0·007 ± 0·003||0·031|
|Raint − 2|| || || || 4·01*||−0·008 ± 0·04||0·027|| || || |
|Temperaturet − 1|| 5·33*|| 0·171 ± 0·074||0·036|| || || || || || |
|Temperaturet − 2|| 8·05**|| 0·115 ± 0·040||0·054|| || || || || || |
|Temperature in previous 3 weeks|| 5·84*||−0·292 ± 0·121||0·039|| 6·32*|| 0·010 ± 0·004||0·042|| || || |
|(Temperature in previous 2 weeks)2||10·7**||−0·011 ± 0·003||0·070|| || || || || || |
|Intercept|| 0·027||−0·031 ± 0·188||–|| 0·069||−0·012 ±||–|| 2·55|| 0·436 ± 0·273||–|
|r2|| 0·522|| || || 0·693|| || || 0·838|| || |
|(b) Covariate||M. domestica||Calliphora spp.||All calyptrate flies|
|F(d.f.=1,144)||Parameter estimates ± SE||Partial r2||F(d.f.=1,149)||Parameter estimates ± SE||Partial r2||F(d.f.=1,140)||Parameter estimates ± SE||Partial r2|
|Temperaturet|| 4·03*||−0·073 ± 0·036||0·027|| || || ||324***|| 0·100 ± 0·006||0·699|
|(Temperaturet)2|| || || || 98·1***|| 0·002 ± 0·000||0·397|| || || |
|Humidityt|| || || || || || || 13·6***||−0·013 ± 0·004||0·088|
|Rain in previous 4 weeks|| || || || || || || 7·93**||−0·055 ± 0·020||0·054|
|Raint – 3|| || || || || || || 5·04*|| 0·022 ± 0·010||0·035|
|(Raint − 1)2|| || || || || || || 4·32*|| 0·002 ± 0·001||0·030|
|(Temperature in previous 2 weeks)2||14·1***|| 0·012 ± 0·003||0·089|| || || || || || |
|(Temperaturet − 1)2||13·2***||−0·007 ± 0·002||0·084|| || || || || || |
|(Humidityt – 2)2|| 4·12*|| 0·001 ± 0·000||0·028|| || || || || || |
|Intercept|| 0·115||−0·096 ± 0·283||–|| 0·224|| 0·015 ± 0·032||–|| 7·35**|| 0·849 ± 0·313||–|
|Model r2|| 0·315|| || || 0·397|| || || 0·776|| || |
|(c) Covariate||M. domestica||Calliphora spp.||All calyptrate flies|
|F(d.f.=1,195)||Parameter estimates ± SE||Partial r2||F(d.f.=1,195)||Parameter estimates ± SE||Partial r2||F(d.f.=1,192)||Parameter estimates ± SE||Partial r2|
|Fly countt−1||78·4***|| 0·535 ± 0·060||0·287||178***|| 0·702 ± 0·053||0·477||114***|| 0·572 ± 0·054||0·372|
|Temperaturet|| || || || || || || 45·6***|| 0·042 ± 0·006||0·192|
|(Temperaturet)2||18·0***|| 0·003 ± 0·001||0·085|| || || || || || |
|(Temperature in previous 2 weeks)2|| 9·58**||−0·002 ± 0·01||0·047|| || || || || || |
|Temperature in previous 3 weeks|| || || || 9·81**|| 0·010 ± 0·003||0·048|| || || |
|Raint|| || || || || || || 7·22**||−0·013 ± 0·005||0·036|
|Intercept|| 2·88||−0·056 ± 0·033||–|| 1·78||−0·045 ± 0·034||–|| 4·62*||−0·110 ± 0·051||–|
|r2|| 0·506|| || || 0·659|| || || 0·844|| || |
For Calliphora spp., numbers were best explained by the mean temperature in the preceding 3 weeks, and there was also an independent negative effect of rainfall in the period 2–3 weeks earlier. For all calyptrate flies combined, as with M. domestica, temperature in the week up to trap collection was the best single predictor, with both rainfall and humidity in this week having smaller negative effects.
The predicted populations for 2003 was closely correlated with the observed fly populations for all three fly groups (Fig. 2). Kleijnen et al. (1998) propose a more stringent test for predictive models of this type, in which the difference between observed and predicted values is regressed against the sum of their values. If the model is accurate there should be no significant relationship. For all pests combined and for M. domestica there was no significant relationship (F1,46 = 2·55 and 0·014, respectively). However, the model for Calliphora failed this test (F1,46 = 21·5, P < 0·001): predictions tended to be slightly higher than observed values in 2003 (Fig. 2).
Figure 2. Predicted and actual weekly catches during 2003. Predictions were produced by examining the relationship between fly numbers and weather variables in 2000–02, and then weather variables were used to predict fly numbers in 2003. Regression of observed vs. predicted numbers: F1,48 = 343, P < 0·001, r2 = 0·880 for M. domestica; F1,48 = 142, P < 0·001, r2 = 0·751 for Calliphora spp.; F1,48 = 1853, P < 0·001, r2 = 0·976 for all calyptrate flies. (a) Musca domestica; (b) Calliphora spp.; (c) all calyptrate flies.
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When models were reconstructed without incorporating previous fly population size, their predictive power was reduced but weather alone was found to provide significant predictive value (compare Table 5a and Table 5b). Once again temperature was the most powerful predictor for all three fly groups, with smaller negative effects of high humidity and rainfall (Table 5). Temperature had a generally positive effect on fly numbers, but for M. domestica there was also a significantly negative quadratic term for temperature in the period 1–2 weeks prior to capture, indicating that very high temperatures may have a delayed negative effect on numbers.
Models based on the full 4-year data set were broadly similar to those evaluated above (Table 5c). These were then used to predict the effects of climate change. For all three fly taxa, the predicted effects of climate change were an increase in abundance (Table 6). Calliphora spp. were least affected by climate change, with predicted increases of abundance of up to 85% by 2080 under the worst-case scenario (and it must be noted that the Calliphora model tended to over-estimate numbers). The models for M. domestica and for all flies combined (which did not overestimate numbers when tested against 2003 data) predicted dramatic increases (Table 6 and Fig. 3). For example M. domestica numbers were predicted to increase by 244% by 2080 under the worst-case climatic scenario, and by 156% under the moderately optimistic medium–low emissions scenario. Increases were most pronounced in the summer months (Fig. 3).
Table 6. Predicted increases in temperature assuming two alternative climate change scenarios from Hulme et al. (2002), and resulting predictions for changes in fly numbers. The ‘medium–low emissions’ scenario is moderately optimistic while that of ‘high emissions’ assumes that attempts to reduce emissions in the future are largely unsuccessful. Predicted fly numbers are expressed as a percentage increase compared to mean fly populations at observed temperatures in 2000–03 and are averages for the entire 4-year simulated period
| ||Medium–low emissions||High emissions|
|Predicted increase (°C)|
|Winter|| 1|| 1·5|| 2|| 1|| 2|| 3·5|
|Spring|| 1|| 1·5|| 2·5|| 1|| 2|| 3·5|
|Summer|| 1·5|| 2·5|| 4|| 1·5|| 3·5|| 5|
|Autumn|| 1·5|| 2·5|| 3·5|| 1·5|| 3|| 5|
|Calliphora||22·1||36·7|| 59·3||22·1|| 50·5|| 85·3|
Figure 3. Actual fly populations for 2000–03 and those predicted by 2050 under two different scenarios of climate change, ‘medium–low emissions’ and ‘high emissions’ (from Hulme et al. 2002; see Table 7). (a) Musca domestica; (b) Calliphora spp.; (c) all calyptrate flies.
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Previous studies have suggested that one consequence of global climate change will be that some insect pests such as codling moth Cydia pomonella, Colorado beetle Leptinotarsa decemlineata and various aphid species will expand their ranges northwards and increase their abundance in parts of their current range (Cannon 1998; Rafoss & Saethre 2003) (although it must be noted that for herbivores there is no clear consensus as to the likely outcome of the complex interactions between climate change, CO2 levels and plant–insect interactions, Newman 2004). A reduction in winter frosts is likely to play a key role in allowing northward advancement of some pest species (Bale 2002).
Our study demonstrates that calyptrate flies are likely to be among the species that respond positively to a warming climate: population fluctuations were strongly determined by the weather and we predict that small increases in temperature can lead to major increases in fly population density. Our most accurate model was for M. domestica, perhaps because this was the only taxonomic grouping used that consisted of a single species. Differential responses to weather of different species within the genus Calliphora and within the large group ‘calyptrate flies’ could lead to inaccuracies in the model. None the less, fly population size could be predicted with reasonable accuracy, despite predictive meteorological data having been obtained from a site some distance away from where the flies were caught.
Fly populations at all of the six monitoring sites were strongly correlated, suggesting that they fluctuate in approximate synchrony. This suggests that insecticidal sprays on landfill sites do not substantially alter the pattern of population fluctuations compared with neighbouring sites, although they may well reduce the overall population in the vicinity.
The key pest species associated with human waste is M. domestica, because it undergoes occasional and spectacular population outbreaks, and because of its propensity to enter human habitations (Imai 1984, 1985; Essa & El Sibae 1993). It is usual for waste disposal sites such as landfills to be sprayed prophylactically with synthetic insecticides (Imai 1985), but this is neither satisfactory nor particularly effective because it poses a health risk to humans and houseflies often exhibit cross- and multiple insecticide resistance (Yasutomi 1966; Hayashi et al. 1977; Keiding 1977; Chapman & Morgan 1992; Chapman et al. 1993; Learmount, Chapman & MacNicoll 2002). Additionally, in outdoor situations large volumes are needed and spray is likely to drift from the target site. We successfully predicted M. domestica numbers from simple and readily recorded weather variables. This could be used to limit prophylactic spraying to periods when climatic conditions are likely to favour fly population increases. Reducing the frequency of spraying may reduce resistance and so render spray applications more effective.
It must be noted that fly catch is likely to be influenced by both fly population size and the weather at the time of sampling, which influences fly activity (Wall, French & Morgan 1992, 1993). If catches are obtained at short time intervals (e.g. every hour) then it is possible to calibrate the catch to take into account the weather, and give a more accurate measure of the underlying fly population (Vogt et al. 1983; Vogt 1992). With weekly sampling such as ours the relationship between fly catch and the underlying population cannot easily be resolved, but the accuracy of our models is reassuring in this respect because it suggests that there is a close relationship between the two. However, we have no way of extrapolating from fly catches to obtain a measure of the actual fly density (flies per unit area) in the study areas.
It remains to be tested whether our models accurately predict fly numbers elsewhere, where biotic and abiotic conditions may be different from the sites in our study (for example on livestock farms). If it does not, then predictive models may have to be tailor-made for particular geographical regions or situations. None the less, our data demonstrate that it is possible to obtain reasonably accurate predictions of fly numbers using local meteorological data. We suggest that this approach may be valuable as part of an integrated pest management programme.
Our models predict that the temperature rises that are expected to occur within the next few decades may result in substantial increases in numbers of pestiferous flies. Overall, numbers of calyptrate flies are predicted to triple by 2080 under the worst-case scenario, and to double even under the fairly optimistic medium–low emissions scenario. Numbers of M. domestica, one of the most troublesome calyptrate species, are predicted to rise even higher. Of course these predictions ignore other climatic changes that may occur but which are less well understood (such as changes in precipitation patterns). As with many other studies that predict increased pest abundance in temperature zones as a result of climate change (Sutherst 1995; Cannon 1998; Baker et al. 2000), our predictions do not take into account biotic factors that may alter in the future (for example changes in incidence of fly parasitoids, competitors and pathogens). None the less, it seems likely that fly numbers will increase in the future and that this will lead to increased nuisance value and enhanced transmission of human pathogens unless improved control measures can be devised. Resistance of many fly populations to insecticides is already high and increasing, and many of the more environmentally damaging pesticides are being withdrawn from use. The options available for fly control are becoming fewer at a time when fly populations associated with human habitations and waste are likely to become greater, posing a serious challenge for the future.