Chapter Eight. Within Field Spatial Patterns: Predator Aggregation in Response to Pest Density.

Transcription

1 Chapter Eight Within Field Spatial Patterns: Predator Aggregation in Response to Pest Density. Such evaluations, whether theoretical or empirical, typically assess enemy performance without any reference to spatial heterogeneity and the spatial dimension of pest and enemy distributions in general. Without this consideration, I would suggest, most studies have missed a very important point. Kareiva (1990) Introduction 224 Predator-pest dynamics and spatial pattern 224 Interpretation of spatial associations 225 Applied aspects of spatial patterns 225 Materials and methods A Preliminary experiment 229 Grid vacuum samples 229 Whole plant controls B Within-field spatial pattern study 2000/01 and 2001/ Parameters assessed in each sampling grid 231 Data analysis 237 Results A Preliminary experiment 239 Spatial patterns and association 239 Whole plant controls 239 Discussion A Preliminary Experiment 245 Results B Within-field spatial pattern 246 Plant damage controls 246 Whole plant controls 246 Whole field patterns 246 Within field spatial pattern 247 Within-field spatial associations 260 Discussion B Within-field spatial pattern 265 Within-field spatial patterns 265 Within-field spatial associations 265 Chapter Summary 267 References

2 Introduction Predator-pest dynamics and spatial pattern The spatial heterogeneity of predator populations is an important component of the ecological theories pertaining to predator-prey dynamics (Kareiva 1990). The aggregation of predators around areas of high prey abundance makes up part of the numerical response of the predator. The numerical response, coupled with the functional response, determines in part the impact of predators on prey populations (fig. 1). Hassell and May (1986) outlined cases in which aggregative numerical response has been demonstrated for a number of arthropod predators and parasites. Kareiva (1987) demonstrated that predator aggregation has an impact of pest control using ladybeetles that aggregated to aphid patches on goldenrod plants. When ladybeetle movement was limited via habitat fragmentation aphid density increased dramatically. One characteristic of a successful biological control agent is that they are able to locate and extinguish patches of high prey abundance, termed spatially density-dependent mortality. Kareiva (1990) suggests that the rate of aggregation is an important factor in prey suppression. If a predator aggregates at a slow rate they may be overwhelmed by local reproduction of the prey. Examples of field studies that investigate spatially densitydependent mortality are rare. Even more unusual are those that include the temporal dimension. Most Australian studies show spatial correlation (positive or negative) between predators and prey at a single point in time but generally ignore the spatial dimension. Stanley (1997) found negative correlations between the most abundant predatory species and Helicoverpa spp. in unsprayed cotton. Overseas studies have used intensive within field sampling combined with spatial pattern analysis to investigate the spatial distribution of predators and prey. Holland et al. (1999) used a two-dimensional grid of pitfall traps to investigate spatial abundance patterns in Carabidae, Araneae, and Collembola. Linyphiidae were relatively homogeneous across the fields but Collembola displayed evidence of clustering. Winder et al. (2001) investigated spatio-temporal dynamics of two aphid species and a predatory carabid at the field scale. There was transient spatial structure for individual species abundance and spatially coupled dynamics between species. Polyphagous predators (carabids and staphylinids) have been shown to aggregate to patches of high aphid density in a wheat field (Bryan & Wratten 1984). Despite these studies there is little direct evidence that predators aggregate to locally high pest densities (Murdoch et al. 1998). 224

3 Interpretation of spatial associations One problem of estimating predator-prey interactions using intensive sampling schemes is that the spatial patterns observed are often difficult to interpret. If two populations are sampled at a single point in time the spatial pattern observed will be influenced by the temporal pattern in predator-prey population interaction and growth (fig. 2). For example, if we were to intensively sample a field at a single point in time, to determine if predators aggregate in response to high prey density, a number of patterns may arise. If the populations were measured when predators locate relatively abundant prey patches you would see high numbers of both predator and prey in the same area (positively correlated). Some time later the predators may have extinguished or reduced this prey patch. At this point in time you would record high numbers of predators but very low numbers of prey in the same area (negatively correlated). Alternatively the two populations may show no correlation in space. As Stanley (1997) notes the absence of significant correlations does not automatically suggest no predatory impacts. The combination of unsynchronised, positive and negative correlations may result in the appearance of no correlation at all. If a significant spatial relationship is observed between predator and prey populations this does not prove that these two populations are impacting upon each other. The two populations may be responding to an independent factor that is also spatially variable such as host plant characteristics or weed density. Despite difficulties with interpretation, spatially explicit, large-scale field studies are necessary for understanding fundamental predator-prey dynamics (Winder et al. 2001) as well as for many applied aspects of pest management sampling schemes. Applied aspects of spatial patterns The within field abundance and activity of beneficial arthropods is often patchy. Dispersion patterns can either be random, uniform or aggregated, the latter being most commonly found in biological communities (Bishop 1981). Pest management sampling schemes suggested for pest and beneficial scouting often assume that the population distribution is random across the field. Evidence from overseas (Holland et al. 1999) and local studies (Scholz 2000) suggest that this assumption is unjustified. The recent interest in precision farming stems from the realisation that it is impractical to view a field as homogenous in terms of yield potential, crop, soil, and growth characteristics (Blackmore 1994). It is expected that fields of the size utilised in Australia (100ha or greater) will show some degree of heterogeneity in both crop characteristics and arthropod distribution. 225

4 Dillon and Fitt (1990) found that the broad scale distribution of Helicoverpa spp. eggs and larvae within a 100 ha cotton field was characterised by small scale clumping. The patchy distribution of eggs and larvae did not consistently occur in any one area of the field. The current scouting procedures in cotton, which rely on randomly selected sampling points, may provide a good representation of pest abundance because the egg and larval distributions do not favour any particular area of the field. Little research has been conducted to determine whether beneficials show similar distribution patterns within a field. Bishop (1978, 1981) found that three spider species were equally distributed in the outer, middle and inner portions of a cotton field throughout most of the season. Bishop (1978) concluded that temporal and spatial distribution patterns of spiders in cotton fields corresponded well with that of their prey, and increased the likelihood of interactions between spider species and their prey. Stanley (1997) investigated a density gradient of arthropods in a cotton field. He compared the numbers of arthropods collected from various sides of cotton fields that bordered bushland or neighbouring crops. Thysanoptera and one Coccinellidae species (Coccinella transversalis Fabricius) displayed minor density gradients from one side of the field to the other, but there was no indication that colonisation occurred from any particular direction. No arthropod samples were collected within putative source areas to determine if they had the potential to supply predators to the adjacent field. Stanley noted that the sampling design utilised, (plots equi-distant from the middle of the field) did not allow the detection of gradients towards the centre of the field. Egg cards have been used to investigate spatial and temporal variation in egg predation and parasitism in sweet corn fields (Scholz 2000). The spatial pattern of predator and parasitoid activity varied from week to week. Trichogramma foraged over the entire field throughout the vegetative stage however the action of Formicidae was restricted to the crop edges. An intensive spatially explicit sampling scheme (grid or transect with locations recorded) is required to answer questions about the spatial patterns of beneficial abundance within fields. Researchers, with limited resources, often cannot use such schemes. Intensive sampling can provide valuable information about pest and beneficial dynamics within the crop. In order to be confident about predicting beneficial abundance within a field from a set of samples taken randomly, we first must have a good understanding of the underlying population distribution. Furthermore, spatial data about predator populations can be combined with physical and 226

5 environmental measurements to reveal a great deal about their ecological requirements. I investigated the within-field spatial patterns of predator abundance, pest abundance, estimates of predator impact on prey using H. armigera egg cards, and plant damage. A grid sampling scheme across the lucerne/soybean interface was used to answer the following questions: 1. Do generalist predators aggregate in patches within a soybean field? 2. What is the size of these aggregations? 3. Does predator aggregation correlate with pest aggregation, plant damage, or predation rate? A preliminary vacuum sampling experiment was conducted to determine which grid size was the best for detecting aggregations of arthropods in a soybean field. A change in the localised abundance of a predator. The result of reproduction, immigration etc. Reproduction Aggregative Response Numerical Response Total Response Developmental Response Functional Response The change in the number of prey consumed by a single predator at varying prey densities. Figure 1. Some factors influencing the population dynamics of predators, adapted from Krebs (2001). 227

6 Figure 2. Some possible spatial patterns of predator and pest abundance and their association in a homogeneous field (bottom maps) given changes in abundance over time (top graph). 228

7 Materials and methods 8.A Preliminary experiment A late season soybean field (Gilbert) was used for a preliminary experiment to determine which grid size would be best for spatial sampling within fields. A nested grid sampling design (fig. 3) was used with three sizes: small scale grid, nine sampling sites with two metres between sites covering an area of four by four metre (4 sites shared with medium, one with large grid); medium scale grid, 36 sampling sites with four metres between sites covering an area of 20 by 20 metres (4 sites shared with large grid); large scale grid, 42 sampling sites with 20 metres between sites covering an area of 100 by 120 metres. Soybean plants were not sown in rows, irrigated when necessary, and no insecticides were applied throughout the season. Grid vacuum samples Seventy-nine vacuum samples were taken on the morning of 30 April The vacuum sampler used was a leaf blower (Echo PB-2105) with a 12cm diameter plastic tube attached over the intake fan. A collection bag (made out of material with 0.5mm mesh size, 20cm diameter opening and 40cm in length) was placed in the nozzle of the tube to trap arthropods and secured with a rubber band. Soybean plants in a 25cm 2 area of crop (three to four plants) were sampled by running the vacuum nozzle up each plant stem and through the foliage. Collecting bags were removed from the vacuum sampler nozzle, sealed and kept chilled whilst being transported to the laboratory. Placing the collection bag in the freezer overnight killed the arthropods. Each sample was sorted under a dissection microscope and stored in 80 percent ethanol. Arthropods were grouped into Araneae, predators, pests, wasps, others and total arthropods for analysis (see Chapter two). Whole plant controls Whole plant samples were taken at eight randomly allocated sampling sites in the large grid. Plant samples were taken at locations adjacent to vacuum samples. Three to four plants (25cm 2 area) were covered with a garbage bag and the stems cut at ground level. The bag was sealed to ensure no arthropods could escape and returned to the laboratory and placed in a freezer. The plants were carefully examined and the arthropods found identified and stored in 80 percent ethanol. Arthropods collected in the whole plant samples were compared to the vacuum samples collected nearby at the same site. 229

8 Figure 3. Sampling design for preliminary experiment in Gilbert soybean field 2000/01. Design consists of three nested sampling grids, small (4m * 4m, n = 9), medium (20m * 20m, n = 36) and large (120m * 100m, n = 42). At each sampling site a 25cm 2 vacuum sample was collected. 230

9 8.B Within-field spatial pattern study 2000/01 and 2001/02 Based on the preliminary set of samples subsequent within field spatial patterns were measured in a grid sampling scheme that consisted of 42 sampling sites located 20 metres from each other in a 100m by 120m grid. Each sampling site was given an X and Y coordinate. The first row of the grid was located within the lucerne field (10m from interface) and the remaining rows were within the soybean field. In total eight grids were sampled (table 1). Three grids in the first season (2000/01) were located in two fields, Mendel (fig. 4) and Horti (fig. 5). Horti field was different to the others in that there was a grassy road separating the soybean from adjacent lucerne. This site had previously been a horticultural cropping area so the grid was divided up into smaller plots by grassy roads (fig. 5). Five grids in the second season (2001/02) were located in two fields, Gilbert A and Gilbert C (fig. 6). A large lucerne field separated both of these fields. The grid in Gilbert A was extended to include the weedy margin on the other side of the soybean (fig. 6). Parameters assessed in each sampling grid Grid sampling commenced one week after the adjacent lucerne field had been cut, dried and bailed. Sampling lasted for five days (the length of time pitfall traps were open). At each of the grid sampling sites a number of variables were measured: 1. Plant-dwelling arthropods Vacuum sampling was used to collect day active, plant-dwelling arthropods and the methods were the same as in the preliminary experiment (above). Sampling commenced as soon as the dew dried off the plants in the morning and was completed within three hours. In order to reduce the time taken to sample the whole grid each row of six points was sampled sequentially from the top to the bottom of the grid. A 25cm row of soybean plants were sampled by running the nozzle of the vacuum sampler up the stems of each plant and through the foliage. Arthropods were sorted, identified (see Chapter two) and stored in 80% ethanol. Whole plant samples were used to assess how representative the vacuum samples were. Methods were the same as in the preliminary experiment except that the whole plant sites were prepared the day before sampling took place to minimise disturbance. One site per row was randomly chosen (seven per grid) and at this site a garbage bag was placed over a 25cm row of soybean (near but not including the vacuum sampling plants). The next day the bag was quickly pulled up over the plants, sealed and stems cut at ground level. Visual samples were collected in the first season to compare to vacuum samples. This involved carefully 231

10 examining a 25cm row of soybean plants for approximately 15 minutes and recording all arthropods. 2. Ground-dwelling arthropods The arthropods inhabiting the ground surface both during the day and at night were sampled using pitfall traps at each grid site. Pitfall traps were dug into the ground between the plants and consisted of a plastic sleeve (7cm in diameter) that was buried flush with the ground surface and remained in the field throughout the season. A plastic drinking cup was inserted into the sleeve and did not project above the ground surface. The cup was filled with 50 percent ethanol and a drop of detergent and left exposed for five days and five nights. All traps were checked throughout the exposure period and filled with ethanol if necessary. A plastic plate held 5cm above the soil surface using nails protected the traps. Arthropods collected in the traps were sieved through a fine mesh (0.5mm) tea strainer in the field then washed into a plastic vial and fresh 80 percent ethanol added. Collection vials were returned to the laboratory and sorted and identified (see Chapter two) under a dissecting microscope. 3. Plant attributes Leaf area loss (percent), pod damage (number of seeds per pod damaged), plant height (cm), and weed density (rank abundance category) were measured at each grid site. Leaf damage was calculated in the field by selecting ten leaves per sampling site and estimating leaf area loss as a percentage using a figure in Colton et al. (1995, see page 51). The whole plant samples were used to assess the accuracy of the leaf damage estimates made in the field. Leaf area loss was recorded from 30 leaves per whole plant sample. In some fields weeds growing between the crop rows had an effect on crop growth and may have had an effect on arthropod abundance. At each sampling site weed density was estimated using a relative abundance measure that ranged from zero (no weeds present), to five (weeds severely stunting crop growth). 4. Egg predation Egg predation at each grid site was assessed using H. armigera eggs on cards attached to the top of a soybean leaf. Preliminary experiments determined the protocol for egg cards use in the field (see Chapter seven). Egg cards, with 20 eggs per card, were made in the morning before use from fresh eggs and older stored eggs if necessary. Cards were placed in the field at 4:00pm and collected at 10:00am the next day (exposed for 18 hours). Observations on egg 232

11 cards were conducted from 4:00pm to 10:00pm and 4:00am to 10:00am if required. Once returned to the laboratory cards were examined under a microscope and numbers of healthy eggs remaining, eggs missing, chewed, and sucked recorded. Cards were maintained in a constant temperature cabinet for one week after exposure to measure parasitism. The percentage of eggs consumed (chewed, sucked and missing) per card, whether or not the card was found by predators (defined as greater than 10% predation per card), and numbers of eggs parasitised were used in data analysis. Table 1. Variables sampled in each of the grids for spatial pattern analysis. Mendel Mendel Horti Gilbert A Gilbert A Gilbert C Gilbert C Gilbert C grid 1. grid 2. grid 1. grid 1. grid 2. grid 1. grid 2. grid 3. Year 2000/ / / / / / / /02 Number of pitfall traps (NA) Number of vacuum samples (NA) Egg predation Y Y Y Y Y Y Y Y Leaf damage Y Y Y Y Y Y Y Y Pod damage - Y Weed density - Y - Y Y Y Y Y Plant height Y Y Y Y Y Visual count Y Y Y Whole plant Y Y Y Y Temp. & Humidity Y Y Y Y Y Y Y Y Dash (-) indicates measurement not be taken, e.g. no pods had developed on plants. Y indicates measurement was collected. NA indicates samples were collected but not sorted or analysed 233

12 Figure 4. Location of grid samples in Mendel soybean field in 2000/01. The grid consisted of 42 sampling sites in a 120m by 100m area. The lucerne was directly adjacent to the soybean field. 234

13 Figure 5. Location of grid samples in Horti. soybean field in 2000/01. The grid consisted of 42 sampling sites. The lucerne was separated from the soybean by a grassy road. The soybean was divided by grassy tracks into blocks 80m by 40m. 235

14 Figure 6. Location of grid samples in Gilbert A and Gilbert C soybean fields in 2001/02. In Gilbert A the grid consisted of 42 sampling sites in a 120m by 100m area. In Gilbert C the grid was extended to the weedy border at the end of the field and consisted of 51 sampling sites. 236

15 Data analysis Whole plant controls: At each grid sampling point the vacuum sample catch was subtracted from the whole plant collection. The mean difference for each arthropod guild (predators, pests etc.) was displayed on a bar chart. A non-parametric paired t-test (Wilcoxon rank-sum test) was used to compare the whole plant samples with the vacuum samples at the same location. For the preliminary experiment there were eight pairs of samples and for the withinfield pattern experiment 18 pairs (six per grid). This analysis was performed on the total arthropods collected, total Predators, total Pests collected and Araneae using the statistical program S-Plus. Plant damage controls: The difference between plant damage estimates obtained in the field and those obtained in the laboratory were compared using a non-parametric paired t-test (Wilcoxon rank-sum test). The data from four grids (six sampling sites per grid) were combined to make a total of 24 paired comparisons. Spatial pattern in counts (SADIE analysis) Spatial patterns in the grid data were examined using Spatial Analysis by Distance Indicies (SADIE). SADIE has been developed for the spatial analysis of ecological data in the form of referenced counts (Perry 1998, Perry et al. 1999). The system works by measuring the minimum effort that the individuals in the population would need to expend to move to a completely regular or uniform arrangement (Perry et al. 1999). A complete description of this methodology is available in Perry (1998), Perry et al. (1999) and the SADIE web site ( A number of indices are produced which quantify the degree of non-randomness within a set of data. The null-hypothesis being tested is that the counts within the grid are arranged randomly with respect to one another. An Index of aggregation (I a ) equals one when the counts are randomly arranged in the grid, but if I a is greater than one the counts are aggregated into clusters. I a and its associated probability (P a ) indicate the overall degree of clustering (P a is the probability that the observed counts are arranged randomly among the given sample units and was considered significant at 0.05 level). Clusters can be in the form of patches or gaps. Patches are defined as neighbourhoods of units with counts that are larger than the overall grid mean. The index V i and its associated 237

16 probability P i indicate the degree of patchiness. If the index V i is greater than one then the units belong to a patch. Gaps are defined as neighbourhoods of units with counts smaller than the overall grid mean. The index V j and its associated probability P j indicate the presence of gaps. If V j is equal to negative one then there is a random arrangement of counts, but if V j is less than negative one then gaps are present. At each x,y coordinate a clustering index is calculated for each data set. The overall spatial association between two data sets can be assessed by examining correlation between the clustering indices of each set. Spatial association between two counts Another SADIE program known as The Quick Association Analysis Shell was used to assess the degree of association between any two parameters measured at the same x,y locations within the grid (see Winder et al. 2001). For example, I test if the spatial pattern in predator abundance and pest abundance is associated (more than you would expect by chance) within the soybean field. The method depends upon calculating the similarity in the clustering indices of two sets of data produced by the initial SADIE test for spatial pattern. A SADIE measure of local spatial association (χ p ) is calculated between the first set of cluster indices and the second set of cluster indices at one x,y point. If χ p is negative (e.g. χ p = -3.0) there is a strong negative (dissociation) between the two data sets at this x,y point. If χ p is positive (e.g. χ p = 4.2) there is a strong positive association between the two data sets at this x,y point. This measure of local association, χ p, may be mapped and contoured to graphically display patterns in association. An overall measure of the spatial association (χ) between the two sets of cluster indices is obtained by averaging all of the χ p values across the grid. An assessment is made of the significance of χ by testing against values χ rand. from a randomisation test. Allowance is made for small-scale autocorrelation in both sets of clustering indices, which reduces the effective sample size. The method of Dutilleul (Dutilleul et al. 1993) is used in this program. The effective size of the combined data sets is computed and degrees of freedom adjusted. Critical limits are inflated by a scale factor, and the significance of the randomisation test adjusted. If the P < there is a significant positive association and if P > there is a significant negative association. 238

17 Only data sets within a sampling grid were compared (not between grids). Mostly those parameters that had shown significant spatial clustering in the first analysis were used in the association analysis. All contour maps were produced in the ArcGIS program package using ArcView. The program produces a contour map by interpolating between two data points. For the edge traps in which there was no adjacent data point this could not be completed. Results 8.A Preliminary experiment There was no difference in the number of arthropods caught per vacuum sample in the small, medium or large grids (fig. 7). The composition of the arthropods in the catch was similar regardless of grid size. The catch was composed mostly of pest species such as N. viridula (green vegetable bug) mirids, aphids and leafhoppers. The Other group was the next most abundant in the vacuum sample catches. The predators consisted mainly of web-building spiders. In total there was 631 arthropods (mean per sample ± standard error, ± 11.38) collected from the nine vacuum samples in the small grid. A total of 2275 arthropods were collected (63.19 ± 5.13) from 36 samples in the medium grid and 2695 (64.17 ± 4.46) from 42 samples in the large grid. Spatial patterns and association Most arthropod groups in the large grid (exception being Wasps) displayed more spatial clustering than would be expected by chance (table 2). That is, these arthropod groups were not distributed randomly within the large grid. In the medium grid clustering was detected only in the Other group and clustering into gaps in the Total group. In the small grid no clustering was detected for any groups (fig. 8). There was a high level of positive spatial association between all arthropod groups within the large grid (table 3, fig. 9). Whole plant controls On average, more arthropods were collected from whole plant samples than vacuum samples (fig. 10). The mean difference in total arthropods caught for eight comparisons was 27. However this difference was not great enough to be significant for total arthropods (P = 0.16), total Predators (P = 0.71) and Araneae (P = 0.52). The greatest difference (of 46 animals) was seen for pest arthropods that were under sampled using the vacuum sampler (P = 0.05). This trend was mainly due to the vacuum sampler s inability to collect aphids off soybean foliage. Wasps and Others were collected in greater numbers by the vacuum sampler than whole plant 239

18 samples. Wasps are easily disturbed and flew away before the whole plant sample bag could be sealed around the plants. Predators, mainly spiders, were equally sampled by both techniques (fig. 10). Mean per sample Small Medium Large 0 Araneae Other Predators Pests Wasps Total Arthropod groups Figure 7. Composition of arthropods collected (number per 25cm 2 vacuum sample) from soybean plants using a vacuum sampler in a small (9m * 9m, n = 9), medium (20m * 20m, n = 36) and large (120m * 100m, n = 42) nested sampling grid. Bars indicate standard error. 240

19 Table 2. Results of the SADIE analysis of arthropod groups collected via vacuum sampling of 25cm 2 soybean plants. Three different sized nested grids were sampled, small (4m * 4m, n = 9), medium (20m * 20m, n = 36) and large (120m * 100m, n = 42). Large n= 42 Gaps Patches Arthropod group mean I a P a mean V j P j mean V i P i Araneae** Predators** Pests** Wasps Other** Total** Medium n= 36 Arthropod group mean I a P a mean V j P j mean V i P i Araneae Predators Pests Wasps Other** Total* Small n= 9 Arthropod group mean I a P a mean V j P j mean V i P i Araneae* Predators Pests Wasps Other Total ** Values are not distributed randomly within the sample grid, there are significant patches and gaps in the arthropod distribution (P < 0.05). * Values are distributed into gaps OR patches only. 241

20 Large 120m * 100m, n=42 Medium 20m * 20m, n=36 20m Small 4m * 4m, n=9 4m 2m Figure 8. Spatial pattern in Predator abundance. At each grid point 25cm 2 of plants were vacuum sampled. The sampling pattern consisted of a nested grid design with small, medium and large grids. 242

21 Table 3. Spatial associations between arthropod groups collected from a soybean field. Arthropods were collected by vacuum sampling a 25cm 2 area of plants at 42 points within a large (100m * 120m) sampling grid. The overall measure of association (χ) and its P were obtained by measuring the similarity between two sets of SADIE clustering indices for each arthropod group. Significant associations are shown in bold. Data set 1 Data set 2 Actual sample size Effective sample size Scale factor χ overall P V Predators V Pests V Araneae V Pests V Other V Pests V Total V Pests V: Vacuum sample of 25cm 2 of soybean 20m Figure 9. Spatial association map between Predators and Pests collected in a large (100m *120m) grid in soybean. Arthropods were collected via vacuum sampling 25cm 2 of plants at each grid point. The index χ p has been mapped which is the SADIE measure of spatial association between Predators and the Pests. Negative values (the white areas) show a disassociation between the two groups and positive values (darker areas) show a strong association. Overall the groups show significant positive association (χ = 0.51, P < 0.01). 243

22 50 40 Mean difference Total Araneae Predators Wasps Pests Other -20 Figure 10. The mean difference between arthropod groups collected via vacuum sampling of soybean plants or whole plant samples. The difference was calculated by subtracting the vacuum sample catch (25cm 2 of soybean plants) from the whole plant sample (25cm 2 soybean plants). The difference was not significant for total arthropods (P = 0.16), total Predators (P = 0.71), Araneae (P = 0.52), but significant for total Pests (P = 0.05). 244

23 Discussion 8.A Preliminary Experiment Preliminary experiments showed that the large sampling grid was suited for answering questions relating to within-field spatial patterns. SADIE analysis could detect aggregation from data collected on a grid with 20 metres between sampling points and a minimum of 42 sampling points. No aggregation could be detected in the medium and small grids. It is clear that spatial patterns of predators within soybean fields operate on scales greater than four metres. Holland et al. (1999) found that grids with 30 metre spacing were optimal for detecting clusters within wheat fields. Furthermore, the numbers of sample units within the grid was important for cluster detection using SADIE. In this study the small (nine sampling points) and medium grids (36 sampling points) were not as useful as the large grid (42 sampling points) that allowed sampling across a wider area (120 by 100 metres). The results obtained from the large grid may be more applicable given field sizes utilized in Australian broad acre cropping systems. Significant spatial association between counts was detected for a number of parameters measured within the large grid. Predators and Pests showed significant spatial association (P < 0.01) (fig. 9). There were regions of positive association in the top left and bottom right hand corners of the grid with a region of negative association in between. This association may be due to the predators aggregating in response to areas of high pest density. The fact that all arthropod groups showed similar significant positive associations suggests that there is an underlying factor that is influencing the abundance patterns of arthropods (Pests and Predators) within this field. This preliminary study did not include a measure of the ground-dwelling arthropods within the soybean field. Subsequent studies assessed these animals at the same spatial scale as the foliage-dwelling arthropods examined here. This assumes that spatial patterns in grounddwelling arthropod abundance will be resolved using the same spatial scale. It is clear that vacuum sampling is capable of detecting most arthropod groups within the soybean foliage. Some groups (the Predators) are sampled more effectively using this method than others (the Pests). Vacuum samples are not intended to be an absolute sample of the arthropods within a set area but rather a relative sample used to compare between sampling points within a grid. 245

24 Results 8.B Within-field spatial pattern Plant damage controls There was some difference in the plant damage estimates between field and laboratory measurements, however the difference was not great, nor was it consistent between sampling grids (fig. 11). There was no significant difference between the mean differences for each paired comparison (P = 0.98). The greatest error was seen in Mendel grid 2 where the field estimates for a single grid point was 11 percent compared to the laboratory estimate of three percent. All other estimates were within four to five percent of the laboratory estimate (0.17% ± 0.53). Whole plant controls Comparisons of arthropods collected via vacuum sampling and whole plant samples in all grids followed the same pattern as for the preliminary experiment (fig.12). On average, more arthropods were collected from the whole plant samples than the vacuum samples for all three sampling grids examined (a total of 18 comparisons). This difference was not significant for total Arthropods (P = 0.34), total Predators (P = 0.20) and Araneae (P = 0.60). The greatest difference (not significant P = 0.12) was seen for pest arthropods that were under sampled using the vacuum sampler. This trend was mainly due to the vacuum samplers inability to collect aphids off soybean foliage. Whole field patterns Predators: In the first season (2000/01) the numbers of predators caught in pitfall traps and vacuum samples (fig. 13) across all grids was greater than for the second season (2001/02). Gilbert C grid 1 had the lowest numbers of pitfall trapped predators and Horti grid 1 had the highest numbers. The high numbers of predators in Horti grid 1 is mainly the result of very high numbers of Dermaptera in this field (mean of 30 per pitfall trap per five days). There was a strong positive correlation between the average numbers vacuum sampled predators and pitfall trapped predators in each grid (fig. 14). Pests: Mendel grid 1 had the highest average number of vacuum sampled pests (59.7 ± 8.9) and pitfall trapped pests (26.6 ± 2.6) (fig. 15). The lowest average numbers of vacuum sampled pests was recorded in Gilbert C grid 1 (18.3 ± 1.9), and pitfall trapped pests in Horti. 246

25 grid 1 (5.6 ± 0.6). Vacuum sample catch decreased as the season progressed in Mendel and Gilbert A fields but the opposite trend was seen in Gilbert C. Egg predation: The greatest average egg predation on cards (56% ± 6.5) was recorded in Gilbert A grid 2. The lowest average (16% ± 4.9) was recorded in Mendel field early season grid (fig. 16). Egg predation on cards increased as the season progressed in Mendel and Gilbert A fields. There was no significant linear relationship between mean egg predation and average spider numbers collected in each grid (vacuum R 2 = 0.24, P = 0.26, pitfall R 2 = 0.29, P = 0.21). Nor did any other predator groups show a strong relationship with egg predation. Mendel grid 1 was the only grid to display some egg parasitism, but the rate was very low (mean 1.52 parasitised eggs per card ± 0.65). Plant damage: Over both seasons plant damage in terms of leaf area loss and pod damage was very low (fig. 17). The highest mean leaf area loss (only 6% ± 0.33) was recorded in Mendel field grid 2. Soybean plants experiencing good growing conditions can compensate, to some extent, for up to 30 percent leaf area loss. At flowering, pod formation and seed development this action threshold is reduced to 15 percent loss of total leaf area (Colton et al. 1995). These thresholds were not reached at any point during both seasons. Pod damage was so low the first season that it could not be analysed. In both seasons pest damage to plants would not have affected yield. Within field spatial pattern The parameters that showed significant amounts of spatial aggregation varied between each grid (table 4) (even for those grids located in the same field at different times during the season). The complete results of the SADIE analysis for each of the eight sampling grids are shown in appendix two, only P a values are shown in table four. Predators: Spatial aggregation of predators was observed in a number of grids. Grounddwelling predators were aggregated in three out of seven grids and the foliage dwelling predators aggregated in four out of seven grids. In Gilbert A grid 1 the ground-dwelling spiders were most abundant in lucerne with decreasing numbers in the adjacent soybean (fig. 18). Foliage-dwelling spiders were more abundant at the other end of the field adjacent to the weedy edge. Lycosidae (captured in pitfall traps) displayed aggregation in four of the seven grids and were consistently most abundant within the lucerne with decreasing trap catch with 247

26 distance from lucrene (fig. 19, see Chapters four and five). The predatory ants were very abundant in the pitfall traps during the first season (Horti. grid 1 mean 84 to Mendel grid 1 mean 89 per pitfall trap) but in the second season their numbers were much lower (Gilbert C grid 1 mean 7 to Gilbert A grid 2 mean 39 per pitfall trap) (fig. 20). Generally the Formicidae were more abundant around the edges of the grids (except in Horti grid 1). Dermaptera reached high numbers in Horti grid 1 (mean 30.1 per trap per week) and were highly aggregated (P < 0.01) in a single patch (map not shown). Pests: Spatial aggregation of pests was observed in a number of grids. Ground-dwelling pests were aggregated in three out of seven grids and the foliage-dwelling pests aggregated in four out of seven grids. In Gilbert C grid 1 pest abundance (in pitfalls and vacuum samples) was highest adjacent to the lucerne interface (at 10m into the soybean) and decreased towards the far end of the grid (fig. 21). Soybean plants were more mature and taller in this area of the field (fig. 22a). The next grid in this area of the field had the lowest abundance of pests (in pitfalls and vacuum samples) (fig. 21) and the plant height difference was no longer evident (fig. 22b). Egg Predation: Predation of H. armigera eggs on cards displayed spatial aggregation in three out of eight grids. The significant spatial pattern seen in Mendel grid 1 was probably an artefact of the very low predation rate overall (fig. 23c). No clear or consistent trends in within-field spatial patterns were observed across all eight grids. In some grids egg predation appeared to be higher in lucerne and decreased with distance from the interface (fig. 23a). Egg parasitism was high enough to analyse in only one grid, Mendel grid 1, where it was not significantly aggregated (P = 0.26). Plant Damage: Leaf area loss showed an aggregated pattern in six of the eight grids; the exceptions being Gilbert C grids 2 and 3. Generally aggregation was due to a single patch of high plant damage in one region of the grid with little to no damage throughout the rest of the grid. 248

27 mean % LAL Whole plant Field observation 0 GilbertA 1. Horti 1. Mendel 1. Mendel 2. Grid sampling site Figure 11. Comparison of plant damage estimates as leaf area loss (%LAL) obtained in the field (n = 10 leaves examined/plant) with those obtained from whole plant samples in the laboratory (n = 30 leaves examined/plant) in each sampling site. Bars indicate standard error Mean difference Total Araneae Predators Pests Wasps Other Figure 12. The mean difference between arthropod groups collected via vacuum sampling of soybean plants or whole plant samples in Mendel grid 2. The difference was calculated by subtracting the vaccum sample catch (25cm row of soybean plants) from the whole plant sample (25cm row of soybean plants). Horti grid 1and Mendel grid 1 displayed a very similar pattern and so their graphs are not included (none significant, see text for details). 249

28 A. Predators in pitfall traps Mean per pitfall trap Whole grid Soybean 0 Horti grid 1. Mendel grid 1. Mendel grid 2. GilbertA grid 1. GilbertA grid 2. GilbertC grid 1. GilbertC grid 2. Sampling grid B. Predators in vacuum samples Mean per vacuum sample Whole grid Soybean Horti grid 1. Mendel grid 1. Mendel grid 2. GilbertA grid 1. GilbertA grid 2. GilbertC grid 1. GilbertC grid 2. Sampling grid Figure 13. The mean numbers of predators caught in A. pitfall traps (open for five days) and B. vacuum samples (25cm of soybean row) across all grids. Black columns show the mean calculated from sampling sites within grid, including those in adjacent lucerne field (n = 42, Gilbert A n = 45). White columns show mean calculated from sites within the soybean crop (n = 36, Gilbert A n = 39). Bars indicate standard error. 250

29 Figure 14. Relationship between predators collected in pitfall traps and vacuum samples in each grid. Each dot represents the average numbers collected per sample across the entire grid (n = 42 or 45, r = 0.94). Mean per sample V pests PT pests 0 Horti grid 1. Mendel grid 1. Mendel grid 2. GilbertA grid 1. GilbertA grid 2. GilbertC grid 1. GilbertC grid 2. Sampling grid Figure 15. The mean numbers of pests caught in pitfall traps (PT) and vacuum samples (V) across all grids. The mean is calculated from all sampling points within the grid, including those in the adjacent lucerne field (n = 42, Gilbert A n = 45). Bars indicate standard error. 251

30 Mean % predation per card All cards Soybean cards 0 Horti grid 1. Mendel grid 1. Mendel grid 2. GilbertA grid 1. GilbertA grid 2. GilbertC grid 1. GilbertC grid 2. GilbertC grid 3. Sampling grid Figure 16. The mean percentage of H. armigera eggs on cards consumed by predators over an 18 hour exposure period. Black columns show the mean calculated from all sampling sites within the grid, including those in the adjacent lucerne field (n = 42, Gilbert A n = 45). White columns show the mean calculated from sites within the soybean crop (n = 36, Gilbert A n = 39). Bars indicate standard error. Mean % leaf area loss Horti grid 1. Mendel grid 1. Mendel grid 2. GilbertA grid 1. Figure 17. Pattern in leaf area loss due to herbivorous arthropods across all grids. Black sites within the soybean crop (n = 36, Gilbert A n = 39). Bars indicate standard error. 252 GilbertA grid 2. Sampling grid GilbertC grid 1. Whole grid Soybean GilbertC grid 2. GilbertC grid 3. columns show the mean calculated from sampling sites within the grid, including those in the adjacent lucerne field (n = 42, Gilbert A n = 45). White columns show the mean calculated

31 Table 4. Summary of SADIE analysis results for data collected in soybean fields adjacent to lucerne. The value shown is the probability (P a ) that that the counts within the grid are arranged randomly with respect to one another. Values less than or equal to 0.05 suggest that there is significant spatial pattern (i.e. the counts are not arranged randomly) (see appendix two for complete results). Parameter Horti Mendel Mendel GilbertA GilbertA GilbertC GilbertC GilbertC grid 1 grid 1 grid 2 grid 1 grid 2 grid 1 grid 2 grid 3 % Eggs predated Egg cards found No. eggs parasitised % Leaf area loss Plant height (cm) Vaccum samples - Total arthropods Pests Other Predators Predatory Hemiptera Syrphidae Aranea e Pitfall trap s Total arthropods Pests Wasps Other Predators Araneae Lycosidae Dermaptera Predatory Coleoptera Carabidae Predatory Formicidae Predatory Staphylinidae Observations Total arthropods Pests iseed: 2104, k5psim: 153 Dash indicates pa rameter not collected or not analys ed due to low numbers. 253

32 A. Foliage-dwelling Araneae P a = 0.01 Soybean Lucerne B. Ground-dwelling Araneae P a < 0.01 Soybean Lucerne Figure 18. Spatial pattern of within field distribution of two parameters measured in Gilbert A grid 1. A. Abundance of Araneae collected by vacuum sampler at each grid point, and B. shows abundance of Araneae captured in pitfall traps open for six days. The solid line shows the edge of the soybean and adjacent lucerne field. 254

33 A. Mendel grid 1 P a = 0.13 B. Gilbert A grid 1 P a < 0.01 Soybean Lucerne C. Gilbert C grid 1 P a < 0.01 D. Gilbert C grid 2 P a < 0.01 Soybean Lucerne Figure 19. Spatial pattern of Lycosidae abundance in pitfall traps in a number of sampling grids. The solid line shows the edge of the soybean and adjacent lucerne fields. Scale indicates numbers captured per trap open for five days. 255

34 A. Horti grid 1 P a = 0.03 (mean 84) B. Mendel grid 1 P a = 0.13 (mean 89) Soybean Lucerne A. Mendel grid 2 P a = 0.16 (mean 86) B. Gilbert C grid 1 P a = 0.13 (mean 7) Soybean Lucerne Figure 20. Spatial patterns in predatory Formicidae abundance in pitfall traps in a number of sampling grids. The solid line shows the edge of the soybean and adjacent lucerne fields. Scale indicates numbers captured per trap open for five days. 256

35 A. Gilbert C grid 1 Pitfall traps P a = 0.01 B. Gilbert C grid 1 Vac. samples P a = 0.16 Soybean Lucerne C. Gilbert C grid 2 Pitfall traps P a = 0.05 D. Gilbert C grid 2 Vac. samples P a = 0.05 Soybean Lucerne Figure 21. Within-field spatial pattern of pest abundance at each grid point in Gilbert C grid 1 and 2. A. and C. ground-dwelling pests and B. and D. foliage-dwelling pests. The solid line shows the edge of the soybean and adjacent lucerne fields. 257

36 A. Gilbert C grid 1 Plant height Soybean Lucerne B. Gilbert C grid 2 Plant height Soybean Lucerne Figure 22. Spatial pattern of within field distribution soybean plant height in Gilbert C grids 1 and 2. Plant height was measured in centimetres at each grid point. The solid line shows the edge of the soybean and adjacent lucerne fields. 258

37 A. Gilbert C grid 1 P a = 0.09 (mean 32%) B. Gilbert C grid 2 P a = 0.05 (mean 40%) Soybean Lucerne C. Mendel grid 1 P a < 0.01 (mean = 16%) D. Mendel grid 2 P a = 0.45 (mean = 38%) Soybean Lucerne Figure 23. Within-field spatial pattern of predation of H. armigera eggs on cards. Shows the percentage of eggs consumed per card at each grid point over 18 hours. The solid line shows the edge of the soybean and adjacent lucerne fields. 259

38 Within-field spatial associations Spatial association analysis for each grid was not based on all possible combinations of comparisons that could have been conducted. Rather testing was restricted to biologically relevant combinations. Parameters that showed significant spatial association varied between each grid (table 5). Complete results of association analysis for each of the eight sampling grids are shown in appendix three. Whilst there were areas of positive and negative association within each grid, across a whole grid the significant associations were mostly positive. In the first season, in Horti grid 1 there was a significant association detected between vacuum sampled pests and vacuum sampled predators (P < 0.01) (both of which were not significantly aggregated within the grid). This association may be an artefact of overall low predator abundance. Predators and pests collected in pitfall traps (fig. 24) did show some positive association, however this was not significant (P = 0.07). In Mendel grid 1 there was a significant positive association between egg predation and pitfall trapped spiders (Araneae P < 0.01 fig. 25, and Lycosidae P = 0.01) (fig. 25). Egg predation (P = 0.02), pitfall trapped spiders (P < 0.01) and Lycosidae (P < 0.01) were also strongly associated (positively) with leaf area loss. A positive association was observed between pitfall trapped pests and spiders (P = 0.04) and Lycosidae (P = 0.04) that was not significant. Mendel grid 1 and grid 2 showed a strong positive relationship between vacuum sampled predators and pests (grid 1 P = 0.02, grid 2 P = 0.01). In Mendel grid 2 there was a strong disassociation between egg predation and leaf area loss (P = 0.97). In the second season leaf area loss was significantly associated with vacuum sampled pests (P < 0.01), predators (P = 0.01) and Araneae (P < 0.01) in Gilbert A grid 1. Vacuum sampled pests and predators were positively associated with each other (P < 0.01, fig. 24) but vacuum sampled pests and Lycosidae were negatively associated (P = 0.99). Egg predation was negatively associated with leaf area loss (P = 0.91) and vacuum sampled predators (P = 0.95) but the relationship was not significant. There were no significant positive associations recorded in Gilbert A grid 2 but a minor positive association between the pitfall trapped predators and pests (P = 0.06) was observed. Vacuum sampled pests and Araneae were significantly negatively associated (P = 0.98). In Gilbert C grid 1 egg predation was positively associated with pitfall trapped Carabidae beetles (P = 0.03) (fig. 25). In Gilbert C grid 1 and 2 plant height displayed significant associations between a number of vacuum sampled 260

39 arthropod groups and some pitfall trapped arthropods (Predators P < 0.01, Araneae P = 0.02, Other P = 0.02) and egg predation (P = 0.03). Whilst in Gilbert C grid 1 plant height and Lycosidae were positively associated (P = 0.01) by grid 2 they were negatively associated (P > 0.975). The opposite pattern was seen for leaf area loss and Lycosidae (grid 1 P > 0.975, grid 2 P = 0.05). The results suggest that there may be biological factors associated with plant height (such as plant complexity or increasing leaf surface area) that may have an impact on arthropod populations. Gilbert C grid 3, with a reduced data set, showed no significant associations but there was a strong negative association between egg predation and leaf area loss (P = 0.92). 261

40 Table 5. Summary of results from the SADIE spatial association analysis. The overall measure of association for two data sets and its P value were obtained by measuring the similarity between two sets of SADIE clustering indices for each arthropod group. P < (positive association) or P > (negative association) are considered significant (see appendix three for complete results). Data set 1 Data set 2 Horti grid 1 Mendel grid 1 Mendel grid 2 Gilbert A grid 1 Gilbert A grid 2 Gilbert C grid 1 Gilbert C grid 2 % Egg Predated % Leaf area loss % Egg Predated PT Predator % Egg Predated PT Carabidae % Egg Predated PT Araneae % Egg Predated PT Lycosidae % Egg Predated V Predator % Egg Predated V Araneae % Egg Predated V Predatory Hemiptera Gilbert C grid % Egg Predated Plant height % Egg Predated V Other V Pest V Predator V Pest V Araneae V Other V Predator V Pest PT Pest V Pest PT Lycosidae V Predator PT Predator PT Pest PT Predator PT Pest PT Dermaptera PT Pest PT Carabidae PT Pest PT Araneae PT Pest PT Lycosidae PT Pest PT Other V Pest Plant height V Predator Plant height PT Predator Plant height PT Araneae Plant height PT Lycosidae Plant height V Araneae Plant height V Other Plant height % Leaf area loss Plant height % Leaf area loss V Predator % Leaf area loss V Araneae % Leaf area loss V Predatory Hemiptera % Leaf area loss PT Araneae % Leaf area loss PT Lycosidae % Leaf area loss PT Other % Leaf area loss PT Pest % Leaf area loss V Pest V: Vacuum sample of 25cm of soybean row PT: Pitfall trap catch Dash indicates data was not analysed 262

41 A. Horti. grid 1 PTPred. vs. PTPest B. Mendel grid 2 VPred. vs. VPest P = 0.07 P = 0.01 Soybean Lucerne C. Gilbert A grid 1 VPred. vs. VPest D. Gilbert C grid 1 PTPred. vs. PTPest P < 0.01 P < 0.01 Soybean Lucerne Figure 24. Spatial association maps for Predators and Pests collected in pitfall traps (PT) and vaccum samples (V) in a number of grids. At each grid point the SADIE measure of spatial association between Predators and the Pests has been mapped. Negative values (the white areas) show a disassociation between the two groups and positive values (darker areas) show a strong association. The solid black line indicates the interface between the lucerne and soybean fields. 263

42 A. Mendel grid 1 Egg Pred. vs. PTAraneae B. Gilbert C grid 1 Egg Pred. vs. PTCarabid P < 0.01 P = 0.03 Soybean Lucerne C. Gilbert C grid 2 Egg Pred. vs. Vpred. P = 0.05 Soybean Lucerne Figure 25. Spatial association map showing relationship between predation of H. armigera eggs on cards and predators collected in pitfall traps (PT) and vacuum samples (V) in a number of grids. At each grid point the SADIE measure of spatial association between two parameters has been mapped. Negative values (the white areas) show a disassociation between the two arthropod groups and positive values (darker areas) show a strong association. The solid black line indicates the interface between the lucerne and soybean fields. 264