Dr David A. Bohan, IACR-Long Ashton Research Station, Department of Agricultural Sciences, University of Bristol, Long Ashton, Bristol, BS41 9AF, UK. Tel: 44 1275 549329. Fax: 44 1275 394007. E-mail: David.Bohan@bbsrc.ac.uk
1. An explicitly spatial sampling approach was employed to test the null hypothesis that the predation on slugs by the carabid beetle Pterostichus melanarius (Illiger) was opportunistic.
2. The beetles and slugs were sampled across a nested series of grids of sampling points, in a field of winter wheat during June and July 1997.
3. The spatial distribution of all slugs in June was found to change with the scale of the sampling grid, from random on the 0.25 m scale, through aggregation at 1 m, to random at 4 m. At the highest scale of 16 m, the slugs were significantly spatially aggregated.
4. The distribution of beetles in June was also spatially dynamic, with randomness observed at the 4 m and 8 m scales. At 16 m, significant aggregation was observed.
5. The dynamic distributions of slugs and beetles, at 16 m, were found not to be associated with, and thus were not determined by, soil or crop factors.
6. Comparison of slug and beetle populations showed, however, that the distributions at 16 m were dynamically associated with each other. In June where there were many slugs there were also many carabids, whilst in July where there were many carabids there were few slugs.
7. Approximately 11% of the beetles sampled across the 16 m grid in June and July were found to have ingested slug protein, following intensive enzyme-linked immunosorbent assay (ELISA) testing.
8. The spatial distribution of these slug-positive beetles was significantly associated with the distribution of the larger slug classes, over 25 mg. Where there were many large slugs in June there were many slug-positive beetles. Conversely, in July few large slugs were found where there were many slug-positive beetles.
9. Parametric analysis revealed that these changes in the large slug class, at each sampling point between June and July (growth), were negatively related to the local numbers of slug-positive beetles, and that growth declined as the local numbers of beetles increased.
10. These findings suggest that predation was not opportunistic, but direct and dynamic, falsifying the null hypothesis. Moreover, this predation elicited significant changes in the spatial distribution and local density of the slugs, in a manner that may be termed spatially density dependent.
Carabid beetles are important polyphagous predators in many ecosystems and have been the subjects of significant ecological research, particularly as beneficial agents in agriculture (den Boer 1977; Thiele 1977; Luff 1987; Lövei & Sunderland 1996). Carabids are considered to be generalist and opportunist predators, which orientate principally by microhabitat cues, feeding on almost any suitable prey they encounter and which they are able to subdue. However, there is growing evidence that carabid beetles orientate towards areas where specific prey species are abundant (e.g. Bryan & Wratten 1984; Wallin & Ekbom 1994). The carabid Pterostichus melanarius is a common polyphagous predator on arable land (Sunderland 1975; Wallin & Ekbom 1988; Symondson et al. 1996; Thomas, Parkinson & Marshall 1998). Symondson et al. (1996) demonstrated that slugs (Mollusca: Gastropoda) were important prey for P. melanarius, by showing that in areas of high slug biomass more beetles were caught in pitfall traps and that these beetles had ingested more slug material than in areas with lower slug biomass. These findings suggested a stronger relationship between slugs and P. melanarius than simply that of opportunism. However, the differences in beetle numbers and slug biomass were related to different cultivation methods, with highest numbers and biomass recorded in the absence of cultivation (Symondson et al. 1996). Thus, it is possible that the association between P. melanarius and slugs could have resulted from both responding independently to the effects of cultivation, and with beetles preying to a greater extent on slugs in less disturbed areas because there simply happened to be more slugs there.
The location of individuals, with respect to each other, is extremely important for the dynamics of ecological interactions (Hassell, Comins & May 1991; Silvertown et al. 1992; Powell et al. 1995; Grenfell & Bolker 1998). The study described in this paper was designed to investigate the spatial association and trophic relationship between P. melanarius and slugs [predominantly Arion intermedius Normand and Deroceras reticulatum (Müller)] in a uniform arable field during June and July, when adult P. melanarius were present at high density (Thomas et al. 1998). We ask whether the locations of the slug and beetle individuals are consistent with slug predation by P. melanarius being opportunistic or whether there is evidence of beetles responding in a more directed manner to the presence of slugs.
Our approach was to map the distribution of slugs and P. melanarius (both the total population of beetles and those that had ingested slug protein) in a field of winter wheat at a number of spatial scales up to a dimension of 80 × 64 m. We believed that, at some of these scales, coherent spatial structuring in the form of slug and P. melanarius patches would become apparent. In this case, opportunistic predation by P. melanarius would be observable as apparent independence between the carabid and slug spatial distribution patterns (sensuPerry 1998). A significant spatial association between the P. melanarius and slug distributions, however, could indicate that either P. melanarius and the slugs were responding to a common spatial variate or factor, or that the carabids were directly responding to slug density. Thus, the formal tests for our stated question are that: (i) there is a statistically quantifiable spatial association between the distributions of P. melanarius and slugs; (ii) the P. melanarius have ingested slug protein; and (iii) the spatial distributions of P. melanarius and slugs are not associated with other spatial variates or factors.
There are, however, considerable statistical problems in using an explicitly spatial approach to test the null hypothesis, resulting mainly from the practical difficulties of sampling for slugs and carabids. Consider an attempt to produce a map of P. melanarius density using a grid of sampling points of pitfall traps. As pitfall trapping relies on the carabids approaching and falling into traps, the numbers trapped do not reflect the absolute local densities of P. melanarius, but rather what has been termed the ‘activity-density’ of the carabids for some distance, or spatial scale, about each trap (Ericson 1977). The scale of the activity-density is a function of the P. melanarius migration rate and how easy it is for P. melanarius to move through the environment (Greenslade 1964; Honek 1988). Across a field of winter wheat, and during a single sampling, these factors might be assumed to be constant. However, even in an apparently homogeneous field, where the scales of beetle activity-density overlap more than one trap, individual beetles could fall into any trap within their range of movement. In this case, all the traps within the scale of activity-density are sampling the same activity-density and would be spatially autocorrelated.
This type of spatial autocorrelation is essentially a problem of the scale of sampling. At a small spatial scale, the traps interfere with each other because the same individual is capable of appearing in any one of a number of traps. At larger spatial scales, the pitfall traps are independent because their scales of activity-density do not overlap, and thus the P. melanarius captured in one trap do not interact with those in other traps. In this case, and all other things being equal, the activity-density of the sample reflects the absolute local density of the carabids, and the sampling is reproducible. At these large spatial scales, a graphical plot of activity-density across the grid of traps produces a map that may be used for analysis of spatial pattern and association and parametric analyses. It is important to note, however, that at these large scales the catches of P. melanarius in the pitfall traps need not be independent. If a P. melanarius patch structure is evident across such scales, then trap catches will necessarily be autocorrelated. It should be stressed, however, that the sampling is independent and the pitfall traps are independently sampling from a common spatial distribution, the patch.
It is not possible, a priori, to define the scale at which pitfall trap catches become independent, particularly because this is dependent upon carabid activity, which, in turn, changes with environmental conditions (Greenslade 1964; Honek 1988; Wallin & Ekbom 1994; Honek 1997). Thus, the lowest spatial scale at which maps may be produced has to be determined using sample data. In this report we have sampled for P. melanarius using a series of nested sampling grids, with a geometrically increasing intersample distance, to establish the scale at which the pitfall traps become independent. We define this as the lowest spatial scale, of those sampled, at which the trap catches are spatially random with respect to one another.
We sampled slug populations using a soil sampling technique that has few of the problems of pitfall trapping and gives estimates of the absolute local density of slugs in the upper 10 cm of soil (Glen, Wiltshire & Milsom 1992; Symondson et al. 1996). Thus, at all spatial scales, plots of the distribution of slugs may be used to describe spatial pattern and association. For parametric analyses, however, we must again invoke the criterion that the sampling is spatially independent. As with sampling for P. melanarius, we employed a geometric series of nested grids to establish the scales of independence for the slugs and produced maps of absolute local slug density.
Materials and methods
The study was conducted in the middle of a flat, triangular 1.7 ha field (field 54, Island Orchard) at IACR-Long Ashton Research Station, with a well-drained loamy soil and bordered by managed hedgerow. During 1996–97, the year of study, field 54 was sown with winter wheat, which was preceded in rotation by winter oilseed rape. The sampling for P. melanarius and slugs was conducted in mid-June and mid-July 1997.
The sampling used a geometric series of grids, based upon a two-dimensional array of 5 × 5 sampling points, with the distance between adjacent points being the spatial scale. For slugs, four spatial scales were employed at 0.25 m, 1 m, 4 m and 16 m (Fig. 1a). The sampling for P. melanarius used only three scales, at 4 m, 8 m and 16 m (Fig. 1b). At the 16 m scale, a 5 × 6 array was used, giving a total experimental dimension within the field of 64 × 80 m (Bohan et al. 1997).
The location of the 16 m grid used for sampling P. melanarius was fixed within the field. Because of the destructive nature of soil sampling for slugs, the 16 m slug-sampling grid was offset from the P. melanarius grid by 2.5 m in an east to west direction (Fig. 1c). For the purposes of analysis though, these grids were considered coincident. The smaller grids were nested within larger-scale grids. The positions of the P. melanarius grids did not change. However, again because of the destructive nature of slug sampling, the positions of the 0.25 m grid within the 1 m grid, the 1 m grid within the 4 m grid, and the 4 m grid within the 16 m grid were determined by random numbers. The position of the 16 m slug grid was also moved by 1.5 m, about the point 2.5 m to the east of the P. melanarius grid, on each sampling date (Fig. 1c).
Pterostichus melanarius sampling technique
The P. melanarius sampling was conducted in the week prior to slug sampling, starting on 16 June and 14 July 1997, respectively. The P. melanarius were captured in paired pitfall traps of 9 cm diameter, 0.25 m either side of each sampling point. The pitfall traps were fitted with galvanized steel-mesh inserts, to separate the carabid beetles from smaller prey items within the trap, and coated with Fluon to prevent P. melanarius from escaping. Each trap was protected from flooding by a plastic rain-cover, which did not impede beetle access to the trap. During sampling, the pitfall traps were opened and emptied every day for 3 successive days. Once emptied, the carabids in the traps were removed to the laboratory for immediate storage at − 20 °C. Subsequently, the carabids were identified and the P. melanarius processed for enzyme-linked immunosorbent assay (ELISA) testing of foregut contents. All P. melanarius collected in each pair of traps, over the 3 days of sampling, were pooled to give the pitfall catch for a particular sampling point.
Slug sampling technique
Slugs are predominantly found in the upper 10 cm of soil (Hunter 1966). These slugs were sampled on 23 June and 21 July 1997 using a soil sampling and flooding technique (Glen et al. 1992), modified from South (1964) and Hunter (1968). A 25 cm × 25 cm×10 cm (length× breadth × height) steel template was pushed into the ground at each sampling point, and undercut with a spade. The undercut soil samples were removed and transported to the laboratory. At the 0.25 m spatial scale, this sampling resulted in the complete removal of the upper 10 cm of soil over an area of 1.25 × 1.25 m. The soil samples were slowly flooded, over 9 days, which forced the slugs to the soil surface, where they were collected daily and individually identified and weighed.
Moisture, temperature and crop assessments
Soil moisture and crop biomass were assessed at each 16 m sampling point across the slug-sampling grid. The percentage moisture of the soil was assessed by extracting a trowel-full of soil from a depth between 7 cm and 10 cm. Each wet sample was then weighed, dried at 110 °C for 2 days, re-weighed and percentage soil moisture was calculated. Crop biomass was assessed from the dry weight of 10 wheat ears sampled at random from each sampling point in the 16 m slug grid. Soil temperature was measured every 3 hours (eight readings per day) at 16 points across the three largest spatial scales of the slug sampling grid, using solid-state, automatic data loggers [Tiny Talk, Gemini Data Loggers (UK) Ltd, Chichester (UK)]. Day-degrees were calculated as the cumulative mean daily temperature at each point, for the 7 days prior to slug sampling.
Enzyme-linked immunosorbent assay (elisa) testing
ELISA testing may be used to detect slug protein antigens in the foregut of carabids, to quantify feeding on slugs (Symondson & Liddell 1993; Symondson et al. 1996). The P. melanarius were dissected to remove the foregut, which was then weighed and homogenized in phosphate buffered saline (× 20 dilution, w/v). These stock solutions were then further diluted × 1000 for use in ELISAs, which followed the protocols described elsewhere for monoclonal antibodies (e.g. Symondson, Erickson & Liddell 1997). Using the general slug monoclonal antibody DrW-2D11 (Symondson & Liddell 1995; Symondson, Mendis & Liddell 1995), ELISA tests were conducted on all the carabid gut samples and were quantified by regression against a dilution series of slug protein standards. Foreguts containing concentrations of antigen greater than 21 ng per ELISA plate well, were considered to be definite positives for DrW-2D11. Pterostichus melanarius individuals that proved positive for DrW-2D11 were deemed to have ingested slug protein in the preceding 2.5 days (Symondson & Liddell 1995; Symondson et al. 1995). The count of these slug-positive P. melanarius at each sampling point was then used for spatial and statistical analysis.
Spatial and statistical analysis
To analyse spatial pattern and association of the data sets, for example across time or between the P. melanarius and slug data, we used the recently developed suite of Spatial Analysis by Distribution IndicEs (SADIE) algorithms (Perry 1995a, b). These find the shortest total distance to regularity for the observed sample by moving the sampled ‘individuals’ between the sample points until the same number is achieved for each. A specified number of simulations is then conducted, where the observed counts are randomly assigned to new sample points, and the distance to regularity calculated for the randomization, to achieve a distribution of permuted distances to regularity. In this way a sample may be assigned an index of aggregation (Ia) and a probability of aggregation (Pa) based upon comparison of the observed distance to regularity with the distribution of permuted distances to regularity. Values of Ia in excess of unity denote spatial aggregation, those approximating unity indicate randomness and those less than unity indicate regularity (Perry 1995a). An extension of this method allows the comparison of two data sets sampled across the same grid to test for association (Perry 1998). Randomizations of a scaled data set, combining the two observed data sets, give an index of association (It) and a probability of association (Pt). Values of It greater than unity indicate spatial association and thus similarity between the observed data sets, whilst values less than unity indicate dissociation. It should be noted that both SADIE tests, of spatial distribution and association, are two-tailed. Pa and Pt should be interpreted accordingly. For all SADIE analyses, a minimum of 400 randomization simulations were conducted with a standard random seed.
Standard parametric techniques (the GLM procedures in Genstat 5.4.1) were also used to describe and model the sample data. Counts were analysed using the Poisson distribution and log link function (McCullagh & Nelder 1989). All other data were analysed using the normal distribution and identity link. The standardized residuals of each model were checked for large deviance, linearity and leverage.
Slug distribution with spatial scale
A total of 1342 slugs were sampled in June and July. At all spatial scales, the sample variance exceeded the mean, indicating overdispersion in the total slug data (Table 1). SADIE showed that the distribution of slugs changed with the scale of sampling. In June, the total slug distribution was spatially random on the 0·25 m scale (Ia= 0·983, Pa= 0·458), showed strong, though not significant, indications of spatial aggregation at the 1 m scale (Ia= 1·248, Pa= 0·078), was random at the 4 m scale (Ia= 0·912, Pa= 0·673), and aggregated at the 16 m scale (Ia= 1·503, Pa= 0·008). The July distributions of slugs showed spatial randomness on all scales (0·25 m: Ia= 0·807, Pa= 0·918; 1 m: Ia= 1·087, Pa= 0·265; 4 m: Ia= 0·963, Pa= 0·536; 16 m: Ia= 1·082, Pa= 0·265), with some tendency towards regularity at 0·25 m. The total slug spatial distribution was clearly spatially and temporally dynamic.
Table 1. . Sample variance and the mean for the total slug data, at different spatial scales, in June and July
The total slug distribution was, however, a composite set of the distributions of seven slug species, with D. reticulatum and A. intermedius making up some 77·1% and 20·4% of the samples, respectively. The remaining 2·5% were A. distinctus Mabille, A. subfuscus Draparnaud, Arion spp., D. caruanae Pollonera and Boettgerilla pallens Simroth. Deroceras reticulatum and A. intermedius had markedly dissimilar spatial distributions. In June and July the distribution of D. reticulatum was characterized by spatial randomness at all spatial scales (Table 2). Conversely, the A. intermedius distributions in June showed spatial randomness at the 0·25 m and 4 m scales, and significant aggregation at the 1 m and 16 m scales (Table 2). In July, spatial randomness was observed on the 0·25 m and 1 m scales, for A. intermedius, and aggregation at the 4 m and 16 m scales (Table 2). A direct comparison of the distribution of both species at each scale, showed that they were spatially independent of one another at all spatial scales (Table 3).
Table 2. . Statistics of spatial distribution of the slugs Arion intermedius and Deroceras reticulatum, at different spatial scales in June and July
Table 3. . Statistics of spatial association for the slugs Arion intermedius and Deroceras reticulatum, at different scales in June and July
Arion intermedius vs. Deroceras reticulatum
Because the spatial distributions of A. intermedius and D. reticulatum showed no association and were spatially random at the 0·25 m scale, the sampling was, by our definition, independent at the lowest spatial scale and maps of the total slug distribution could be made at all scales. These maps of slug density could then be used both for the analysis of spatial pattern and association, and for parametric analyses.
Pterostichus melanarius distribution with spatial scale
During June and July, a total of 1464 P. melanarius were sampled by pitfall trapping. This represented 97% of all large carabids trapped (Nebria brevicollis Fabricius or larger). The P. melanarius counts were highly overdispersed, at all spatial scales, in June and July (Table 4). The spatial structuring of the P. melanarius counts, however, was found to change with spatial scale. At the scales of 4 m (Ia= 0·886, Pa= 0·695) and 8 m (Ia= 0·847, Pa= 0·824), the June distribution of P. melanarius was spatially random, although with some tendency towards regularity. As with the total slug distribution, at the 16 m scale, the June distribution of P. melanarius was significantly aggregated (Ia= 1·524, Pa= 0·007). The July distribution of P. melanarius was random at all spatial scales (4 m: Ia= 0·875, Pa= 0·776; 8 m: Ia= 1·056, Pa= 0·320; 16 m: Ia= 0·822, Pa= 0·909).
Table 4. . Sample variance and the mean for Pterostichus melanarius, at different spatial scales, in June and July
The random distributions of P. melanarius at the 4 m scale indicate that the beetle counts in each pitfall trap were independent of one another, and showed no spatial autocorrelation. Therefore, samples from the spatial scales from 4 m upwards could safely be mapped and used for analysis of spatial pattern and association, and parametric analyses.
Spatial association of p. melanarius and slugs
For study of the possible trophic association between the slugs and carabids, we considered the 16 m scales alone, where the sampling of P. melanarius and the slugs coincided.
During June and July, a total of 523 slugs and 739 P. melanarius were sampled across the 16 m scale grids. The total slug counts per sample showed spatial aggregation in June (Ia= 1·503, Pa= 0·008, Fig. 2a), with a focus of crowding in the central-West of the sampling grid, and spatial randomness in July (Ia= 1·082, Pa= 0·265, Fig. 2b). In June, the counts of P. melanarius were also aggregated, with a focus in the central-West of the sampling grid (Ia= 1·524, Pa= 0·007, Fig. 2c), whilst in July they were random with a tendency to regularity (Ia= 0·822, Pa= 0·909, Fig. 2d). These patterns of slug and P. melanarius distribution were found to be independent, and thus not directly determined by soil and crop factors. The distribution of slugs was independent of crop biomass (June: It= 0·982, Pt= 0·855; July: It= 0·990, Pt= 0·659) and neither the slugs (June: It= 0·970, Pt= 0·603; July: It= 1·064, Pt= 0·141) nor carabids (June: It= 1·003, Pt= 0·689; July: It= 1·013, Pt= 0·440) were associated with soil moisture. From June to July, across the 16 m scale, the mean temperature at sampling increased from 12·8 to 15·9 °C. The day-degrees accumulated in the 7 days before sampling were highly regular, at all spatial scales in both months (Table 5), implying that the temperature was similar across the grids, a pattern that cannot explain the dynamics of slug distribution.
Table 5. . Variance and the mean for cumulative day-degrees of temperature, at different spatial scales, in June and July
Indications of association were found between the total slug and the P. melanarius distributions in June (It= 1·489, Pt= 0·006, Fig. 2a,c), suggesting that where there were more slugs in the field there were more carabids. In contrast, by July dissociation was apparent (It= 0·773, Pt= 0·959, Fig. 2b,d), and thus where there were more P. melanarius there were relatively few slugs. Comparing P. melanarius and A. intermedius showed that the distributions were independent, both in June (It= 1·261, Pt= 0·101) and July (It= 1·048, Pt= 0·355). The distributions of P. melanarius and D. reticulatum were, similarly, independent in June (It= 1·046, Pt= 0·258), although strongly dissociated in July (It= 0·883, Pt= 0·991). Thus it would seem that the association between P. melanarius and slugs was not to any individual slug species, but rather to the total slug distribution.
The change in the P. melanarius–total slug association, from June to July, appeared to result from a change in the distribution of slugs. Whilst the total slug distributions in June and July were truly independent of one another (It= 1·035, Pt= 0·443, Fig. 2a,b), the P. melanarius distributions were more similar to one another (It= 1·110, Pt= 0·173, Fig. 2c,d). This would suggest that the distributions of slugs had significantly changed over the period from June to July, whilst there was relatively little change in the P. melanarius distribution.
Trophic association of p. melanarius and slugs
In the absence of soil and crop associations, the simplest explanation for the changes in the carabid and total slug distributions would seem to be slug predation by P. melanarius. To investigate whether there was any evidence of a trophic interaction, the foreguts of all P. melanarius collected in June and July were subjected to ELISA testing. Of the 739 P. melanarius sampled, 11·1% were found to have ingested slug protein (henceforth + ve carabids). Many predators have both upper and lower limits on prey size choice, and this has been demonstrated for carabids feeding on molluscs (Greene 1975; Loreau 1984; Digweed 1993). Laboratory research suggests that P. melanarius mainly tackle individuals below 50 mg in weight (A. McKemey personal communication). Therefore, the slug samples were successively stratified by individual weight for comparison with the + ve carabid distribution. Interestingly, there was no association between + ve carabids and slugs smaller than 25 mg (June: It= 1·102, Pt= 0·321; July: It= 1·081, Pt= 0·368). Rather, larger slugs (above 25 mg), making up some 69·7% of the combined June and July samples, produced significant spatial associations. In June the + ve carabids and larger slugs were spatially associated (It= 1·281, Pt= 0·021, Fig. 3a,c), whilst in July the + ve carabids and larger slugs were significantly dissociated (It= 0·755, Pt= 0·979, Fig. 3b,d). Thus, where larger slugs were more abundant in June there were more carabids that had ingested slug protein, whilst in July where there were more + ve carabids, few larger slugs remained. Thus, the dynamics of the spatial interaction between slugs and P. melanarius, would appear to have been associated through predation on larger slugs.
Parametric relationship between p. melanarius and slugs
In common with the SADIE analyses of spatial distribution and association, the absolute local densities of slugs and carabid numbers were found to be related. In June, the logarithm of local numbers of P. melanarius was directly related to the total slug population density (Fig. 4a), whilst in July, the local densities of the total slug population were negatively related to the logarithm of local beetle numbers (Fig. 4b). The logarithm of the proportional change in the density of total slugs, between June and July [effectively a k-factor (Varley & Gradwell 1970) which for simplicity we have termed growth], was found to be negatively related to the local number of beetles in July (Fig. 5a, P= 0·055). Growth declined with increasing P. melanarius numbers, becoming negative, on average, above a threshold of about 25 beetles per sample. The SADIE approach showed that slugs and carabids appeared to be associated through P. melanarius predation on slugs larger than 25 mg. Recalculating and plotting growth, for the larger slugs alone, against the local number of slug + ve P. melanarius, showed that growth of larger slugs was negatively related to the number of slug + ve beetles (Fig. 5b, P= 0·03). Thus, as the local number of slug + ve beetles increased, the population growth of larger slugs declined. From this, it would appear that predation drives, at least in part, the changes in local population growth and the dynamics of the spatial association.
Spatial analysis of the slug and P. melanarius sample data showed the sampling points to be independent at all scales. Thus, the sampling points yielded data that were true and independent reflections of local numbers. On the 16 m scale, local numbers of slugs and P. melanarius were statistically associated in June. Where there were more slugs there were more P. melanarius. In July, by contrast, spatial dissociation was observed. High local numbers of P. melanarius were associated with low local densities of slugs.
Predation was found to be a potential mechanism for this change in the association of the slugs and P. melanarius. Moreover, there was evidence that this predation was specific to certain slug size classes. ELISA analysis of the gut contents of each P. melanarius showed that approximately 11% of the individuals sampled had ingested slug protein. The spatial patterns of these slug + ve carabids were not associated with the smallest slugs, but rather with larger slugs above 25 mg. Where there were many larger slugs, in June, there were many beetles that had ingested slug protein. In July, where there were many + ve P. melanarius there were few large slugs. Importantly, however, this trophic association was not slug species-specific. Despite the different distributions of the two predominant slug species, A. intermedius and D. reticulatum, the slug + ve P. melanarius were associated with the larger individuals of a ‘set’ of all slug species.
We found no evidence that the association between the slugs and beetles was a consequence of both distributions being associated with a common variate or factor (crop biomass, soil temperature and moisture), rather than a direct interaction. Considered together, these separate lines of evidence would argue that the spatial dynamics of P. melanarius and slug abundance were not the result of an association to a common soil or crop factor, but a specific, trophic association between the beetles and slugs.
That the beetles were not associated with a single species of slug, but to the set of slug species, suggests that P. melanarius could define the set, ‘slugs’, and that they were specifically associating with it. As this association was found to be trophic, and not associated with soil, crop or temperature factors, it would indicate that the central question of this paper as to whether the interaction of P. melanarius and slugs is opportunistic has been tested. We conclude therefore that the interaction between P. melanarius and slugs was not opportunistic, but rather a direct association.
Parametric analyses confirm the suggestion from the SADIE analyses that predation is a mechanism for the interaction. However, significant sources of error could confound both analyses. In particular, because of the destructive nature of the slug sampling, the location of the slug and P. melanarius sampling was necessarily offset spatially, and the pitfall trapping was undertaken in the week prior to slug sampling. Yet, for analysis, the grids and assessment dates were treated as coincident. In addition, sampling resulted in removal of both P. melanarius and slugs. Thus, an implicit assumption in the analysis was that the sampling had negligible effects on the overall abundance and local distributions of slugs and P. melanarius.
Despite these sources of error, parametric analyses showed that the gross changes in the slug population, as measured by what we have termed growth, were related to numbers of predatory beetles and the numbers of predators that were deemed to have ingested slug protein. As for the SADIE analysis, the absolute local densities of the total slug population were directly related to the local numbers of beetles in June, whilst in July P. melanarius numbers trapped were negatively related to the local absolute density of slugs. This change in the local densities of slugs, between June and July, was found to be related to P. melanarius numbers (P= 0·055) and was presumably caused by predation. To test this, the local numbers of slug + ve beetles in July were fitted to slug growth, recalculated for the larger slug size classes alone. A strong, negative relationship was evident between growth in the population of larger slugs and the July numbers of + ve P. melanarius, which would argue that predation was the mechanism for both the changes in slug distribution and the gross changes in local slug density.
Slug growth is a compound statistic that contained the systematic errors discussed earlier and changes in the slug population that resulted from slug birth, death, immigration and emigration. Our findings indicate that not only was the predation interaction robust enough to be observable, despite the systematic errors in the data, but that predation on the larger slugs could have been one of the most important influences on slug population dynamics during June and July.
This analysis describes a spatial predation interaction in which the predator, P. melanarius, detects locales of high slug density and aggregates to them. Subsequent predation, on the larger slugs by the beetle, significantly modifies the distribution of the slugs but, interestingly, does not result in the redistribution of P. melanarius. It is known that adult P. melanarius start to emerge, in southern Britain, in mid- to late-May (Thomas et al. 1998). These individuals typically exhibit rates of diffusion that are low, relative to their potential rates of movement (Thomas et al. 1998). This low diffusion parallels the observation that the spatial distribution of P. melanarius did not change markedly between June and July. However, it explains neither this low vagility nor how the beetles identify patches of high slug density.
It is possible to construct a hypothesis to explain this. During an initial period of search, P. melanarius come into contact with and prey on slugs larger than 25 mg. This causes the beetles to arrest within that locale. This behaviour could lead to the spatial association between the slugs and + ve P. melanarius that was observed in June. Should the arrestment behaviour last for the period between slug meals, a period that could be defined as beetle satiation time plus a limited period searching for the next prey item, then over all beetles the spatial distribution will persist as some function of the satiation time. If all beetles are assumed to be capable of taking slugs, and given that approximately 11% of beetles are slug + ve, a state which may last for 2·5 days (Symondson & Liddell 1995), then this implies that satiation could last for up to 23 days after a slug meal; a period that could explain the observed long-term stability of the P. melanarius distribution. However, we can only speculate about the natural length of satiation and arrestment, and the importance of these two behavioural mechanisms.
This work was funded by the Biotechnology and Biological Sciences Research Council and the Ministry of Agriculture, Fisheries and Food of the United Kingdom. In addition the authors would like to thank Professor Joe Perry, Dr Phil Brain and Dr George Thomas for their comments and encouragement during this research.
Received 23 April 1999;revisionreceived 19 July 1999