Non-crop vegetation areas in agricultural landscapes are vital for maintaining biodiversity. However, they potentially host pest insects, which can cause economic loss in crop fields.
Some insect species have been found to spill into crops from these areas, but this varies depending on species, landscape composition and the time of the season.
To determine if five common pest insects of canola crops were spilling into fields during the late growing season, we collected samples at various distances from non-crop areas, in a part of the Canadian Prairies (Alberta, Canada) where this crop is widely grown.
Sampling occurred at 15 sites in each of 10 fields (N = 150 sites). We modelled changes in pest abundance over distance from the non-crop areas and contrasted the abundance of each taxon in the crop and non-crop areas.
Only leafhoppers (Hemiptera: Cicadellidae) demonstrated a declining gradient in abundance that is consistent with spillover from non-crop vegetation areas into the canola fields. Weevils were found to have significantly higher abundance in the non-crop areas, indicating a relationship between this taxon and the non-crop area in the late season, but there was no decline in abundance, which might indicate spillover occurring. All taxa demonstrated spatial differences in abundance among fields.
This study found limited evidence that the pests are spilling over from non-crop vegetation into canola crops during the late season. Therefore, movement of pests from non-crop vegetation areas at this time is unlikely to be a driver of pest pressure for this economically important crop.

INTRODUCTION

Intensive agricultural practices negatively impact biodiversity on a global scale (Plantureux et al., 2005; Tscharntke, Klein, et al., 2005). In the face of these declines, maintaining biodiversity in agricultural landscapes is increasingly recognized as a priority (Díaz et al., 2019; IPBES, 2022; Tscharntke et al., 2016). Non-crop vegetation areas in croplands may be essential for maintaining biodiversity in these otherwise intensively managed landscapes (Benton et al., 2003; Estrada-Carmona et al., 2022; Robinson et al., 2021). These uncultivated areas are interspersed within and adjacent to crops (Shapira et al., 2018) and often consist of semi-natural land covers such as trees, grass, wetlands and ponds, which can serve as important habitat; for example, as foraging (King et al., 2015) and stopover sites for migratory birds (Belaire et al., 2014). Non-crop vegetation areas also act as reservoirs for beneficial arthropods, such as spiders, beetles and native bees (Bianchi et al., 2006; Galpern et al., 2021; Robinson et al., 2021; Vickruck et al., 2019). These arthropods have been shown to provide ecosystem services, such as pollination and pest predation, which can be beneficial to crops (Bianchi et al., 2006; Tscharntke, Rand, & Bianchi, 2005; Vickruck et al., 2019). However, some research suggests that non-crop vegetation areas may also be reservoirs for insect pests (Tscharntke et al., 2016; Tscharntke, Klein, et al., 2005).

Herbivorous pest insects are known to cause considerable damage in many crops, resulting in more than 80% loss in some crop types (Oerke, 2006). In this study, we focus on canola, an economically important crop in the Canadian Prairie region (Dosdall & Mason, 2010). Pest insects have been known to cause more than $300 million in loss annually in canola crops in North America (Dosdall & Mason, 2010). Insect herbivores cause damage by feeding on the crop (Dosdall & Mason, 2010) and spreading pathogens, which changes plant physiology, reducing crop yield (Soroka et al., 2015; Trivellone et al., 2021). In many crops, insect herbivores have been shown to cause significant damage continuously over many growing seasons, not just during periodic outbreaks (Dosdall & Mason, 2010). Common insect herbivores present in canola include species of Coleoptera, Hemiptera and Lepidoptera (Dosdall & Mason, 2010). If the life cycles of herbivorous insects are supported by non-crop vegetation areas, these areas could be acting as reservoirs, with the potential for pest insects to spill from these areas into crops at certain times of the season and increase the pest pressure. Spillover effects have been demonstrated in both beneficial and pest species, by measuring changes in abundance at different distances from non-crop areas (Tscharntke, Rand, & Bianchi, 2005; Vickruck et al., 2019). Many of these studies have been conducted in Europe or the United States (Eckberg et al., 2015; Goethe et al., 2021; Shapira et al., 2018; Tonina et al., 2018); however, pest spatial distribution with respect to non-crop vegetation has seldom been studied elsewhere. Changes in herbivore distribution at different distances from non-crop vegetation areas can be used to determine if spillover is occurring, and if herbivores rely on these non-crop vegetation areas at the time of collection.

Previous studies have shown that there is considerable variation in the abundance and distribution of different insect herbivores within a crop field (Karp et al., 2018). For example, canola cabbage aphid (Brevicoryne brassicae L.) abundance declines at greater distances into the crop from the field margins (Severtson et al., 2015), while cabbage seedpod weevils (Ceutorhynchus obstrictus Marsham) have a more homogeneous distribution throughout the field (Dosdall et al., 2006). These variations could be due to a variety of factors, such as landscape composition or configuration (González et al., 2022; Karp et al., 2018). Previous research on this topic has typically considered a single focal crop pest (Dosdall et al., 2006; Severtson et al., 2015; Tonina et al., 2018), rather than an assemblage of economically important pests.

Different times of the growing season will influence spillover in insects (Dosdall et al., 2006; Robinson et al., 2021). Depending on the lifecycle of an insect species or how an insect taxon is utilizing the non-crop area, insects could be spilling out of the non-crop areas at multiple times throughout the growing season (Hatchett et al., 1987). Looking at the distributions of these insects later in the growing season provides insight on if pests are relying on these non-crop areas at that late period, or if they gain most resources from the crop at that point in time. Furthermore, because some pest taxa cause damage to the quality of canola seed pods, knowing their distribution and potential for spillover in the late season, when canola seed pods are maturing, is important (Gavloski et al., 2011).

In this study, we explore the late season distribution of five common insect pest taxa present in canola fields in central Alberta, a region of the Canadian Prairies, to determine if these herbivores are spilling into canola fields from non-crop vegetation areas. We adopt a sampling design, informed by a spatial algorithm, with the goal of ‘mapping’ the abundance of taxa across fields by collecting data at multiple locations throughout each field stratified by distance from the field margins. We focus on five taxonomic groups that include species known to be of economic importance to canola: flea beetles (Coleoptera: Alticini), Lygus ssp. (Hemiptera: Miridae), weevils (Coleoptera: Curculionidae), caterpillars (Order: Lepidoptera) and leafhoppers (Hemiptera: Cicadellidae). These groups also represent a broad range of above-ground feeding guilds in the region so that we can explore the response of these pest groups to the landscape (Gavloski et al., 2011).

We predict that because these taxa may be attracted to the crop specifically for its foraging value: (a) more pest herbivores will be observed in the crop field relative to the adjacent non-crop vegetation area (P1); and, (b) because of the even distribution of the crop as a foraging resource within a field, these taxa will not exhibit a decline in abundance at increasing distances from the non-crop vegetation areas, and instead will demonstrate no change at different distances from the non-crop vegetation areas (P2). Findings consistent with these predictions would demonstrate that pest taxa are not relying on non-crop vegetation areas in and around the crops during the late growing season and therefore that there is no increased risk to canola crops caused by pests, which may be harbouring in adjacent non-crop vegetation areas during this period.

METHODS

Study area

Insect samples were collected from 10 canola fields in the Aspen Parkland Ecozone of central Alberta, Canada, from early to mid-August in 2021 and 2022. The samples collected in 2021 were from three canola fields located near 52.381^oN, 113.795^oW (Fields A–C; Figure 1). The samples collected in 2022 were from seven canola fields located near 51.657^oN, 114.137^oW (Fields D–J; Figure 1). All fields were treated with herbicides and insecticides early in the growing season at the discretion of the growers. The insecticidal treatments included deltamethrin foliar spraying across whole fields and neonicotinoid seed treatments. Other common crops in this area included barley and wheat, as well other cereals, and pulses. Some of these crops, such as barley, are not hosts of canola pests while canola likely shares pests with other crops such as mustard (Lygus spp., leafhoppers, caterpillars, flea beetles and weevils) and flax (Lygus spp., leafhoppers and caterpillars; Gavloski et al., 2011). However, crops such as mustard or flax are grown in less abundance than canola, wheat or barley in this region. Non-crop land covers within and near fields in this intensively cropped dryland farming region are typically classified as trees, grasslands, shrub and wetland areas (Alberta Parks, 2015), although shrub-dominant land covers were not found in direct vicinity of the fields in this study. Our non-crop vegetation samples were taken from both treed and grassy areas but not wetlands. Treed areas and grassy areas are primarily distinguished by the presence or absence of trees, the most common of which is Aspen (Populus tremuloides). While the species compositions of herbaceous species may be somewhat different between treed and grassy areas in this region, the most marked difference is in the relative abundance of the species and not their presence or absence (Bird, 1961). Polygons describing the outline of non-crop vegetation within or immediately adjacent to field boundaries were manually digitized (Figure 1) from recent high-resolution full-colour imagery (2021; Google Earth, 2023 CNES/Airbus, Maxar Technologies).

Details are in the caption following the image — **FIGURE 1**
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Sampling maps of 10 canola fields (A–J) in central Alberta where pest herbivore taxa were collected. Sites (N = 150) were systematically positioned using an algorithm that covered a range of distances from the field margin while maximizing their separation. The classes of non-crop vegetation and their distribution with respect to the crop (white areas) are shown. Digits indicate the distance of a sampling site from the nearest field margin (i.e., non-crop vegetation area; in m). Field maps are shown at approximately the same scale.

Sampling methodology

A custom algorithm written in R was used on each field to find 15 sampling locations in and at continuous distances from non-crop vegetation areas while attempting to maximize the distance from the nearest neighbouring sampling location. This spatially explicit approach was intended to ‘map’ herbivore distributions within the field, permitting the statistical estimation of a spillover effect, while controlling for higher or lower zones of herbivore abundance, which may exist. The results of this site selection algorithm as applied to each field are shown in Figure 1 (each field had six collection sites selected within non-crop vegetation areas and nine within the crop). This algorithmic approach, rather than using more traditional linear transects, allowed for efficient whole-field mapping and consistent comparison between distances from multiple fields that have differing amounts and configurations of non-crop vegetation (Figure 1). Sites were precisely located (±5 m) in the field using a GPS unit where a pitfall trap was deployed for 14 days (a plastic cup buried to soil level and partially filled with 100% propylene glycol as a preservative). Sweep net samples were collected at the time of pitfall retrieval using the protocol outlined by the Canola Council of Canada (Proper sweep net technique, 2017). Ten sweeps were taken at each site and combined to represent a single sample. In total, we collected two different insect samples at each of 15 sites at each of 10 fields (i.e., 300 samples at 150 sites).

Lab analysis

The insect herbivore taxa chosen for this study were selected based on their economic importance in the region, high abundance in the samples from early to mid-August, and to represent a broad range of the available above-ground feeding guilds in the region while maintaining functional guild specificity within each taxon (Gavloski et al., 2011). These insects were sorted out of the samples and identified to the lowest feasible taxonomic level: flea beetles (Coleoptera: Alticini), Lygus ssp. (Hemiptera: Miridae), weevils (Coleoptera: Curculionidae), caterpillars (Order: Lepidoptera) and leafhoppers (Hemiptera: Cicadellidae). After being sorted out, each was counted to determine abundance at different points in the field.

The taxa chosen were not identified to species, though there are common pest species in the area that are most likely present in the samples. For flea beetles, this includes Phyllotreta cruciferae (Goeze), Phyllotreta striolata (Fabricius) and Psylliodes punctulata (Melsheimer) (Gavloski et al., 2011). For Lygus spp., this includes Lygus keltoni (Schwartz), L. lineolaris (Palisot de Beauvois), L. elisus (Van Duzee) and L. borealis (Kelton) (Gavloski et al., 2011). For caterpillars, this includes Autographia californica (Speyer), Mamestra configurata (Walker), Discestra trifolii (Hufnagel) and various cutworm species (Gavloski et al., 2011). For weevils, this includes C. obstrictus (Gavloski et al., 2011). For leafhoppers, this includes Macrosteles quadrilineatus (Forbes), Neokolla hieroglyphica (Say), Scaphytopius acutus (Say), Euscelis maculipennis (DeLong and Davidson), Diplocolenus configuratus (Uhler), Sorhoanus ulheri (Oman), Ceratagalia humilis (Kirkaldy) and Amplicephalus inimicus (Say; Gavloski et al., 2011). The overall low taxonomic diversity of herbivorous insects in the region's canola cropping system and the presence of highly dominant species dictated the taxonomic resolution, which would be feasible for identification in this study (Gavloski et al., 2011). As such, we analysed data at a taxonomic resolution like the one at which crop growers would use in assessing risk to their crops. While we cannot be certain that all species within a specific taxon are indeed a pest, many species within these groups are known pests of canola (Dosdall & Mason, 2010; Gavloski et al., 2011), and its presence in or near the crop increases the likelihood that it is a pest taxon. In some cases, the pests may also not be known to the species level and are identified at the higher taxonomic levels (Gavloski et al., 2011). Other taxa present in the samples, such as most beetles, grasshoppers, aphids and root maggots, were excluded because they were not sufficiently abundant, or, in the case of most beetles, required additional taxonomic resolution to confirm their herbivorous status.

Statistical analysis

All data preparation and statistical modelling used R (R Core Team, 2021). Both predictions (i.e., P1 and P2) were tested with negative-binomial generalized additive models (GAMs), using the mgcv package in R (Wood, 2017). This approach is suitable for modelling count data with spatial, random and non-linear effects (Wood, 2017). Including a random effect of field is important to account for field-wise mean differences in abundance, which may be due to differing management practices such as spraying, or differences in the total amount of non-crop area among fields. Models used the count of insects in a sample as the response variable and were run separately by taxon. Models testing P1 used all sampling sites and included a categorical term for whether the sampling site was in-crop or at non-crop vegetation, while models testing P2 used only in-crop sites and included a smooth on the distance from the non-crop area (i.e., the nearest crop margin). All models included a smooth for each field, entered as the tensor product of the centred northing and easting coordinates of the sites, intended to improve conditional independence among sampling stations and to control for within-field spatial variability in abundance. Finally, all models included a term to model expected mean differences among pitfall and sweep sampling methods. Expected random differences among fields in mean abundance were modelled using a random intercept, which can be approximated using a smooth term (Wood, 2017). Within-field spatial and distance smooth terms were modelled with a thin-plate shrinkage spline that accomplishes automatic variable selection simultaneously (Marra & Wood, 2011), effectively removing the smooth from a model if it is not a significant predictor of abundance. Goodness of fit was assessed by simulating residuals from each fitted model using the DHARMa package for R (Hartig, 2022). Tests included Kolmogorov–Smirnov test of the normality of the residual distribution, a test for over- or underdispersion and a test for the presence of outliers. To verify that model terms had captured spatial variability, and therefore that within-field sampling stations were conditionally independent, we checked for residual spatial autocorrelation using a Moran's I test on the simulated residuals. This was done separately for sweep net and pitfall sample data, also using the DHARMa package (Hartig, 2022).

RESULTS

There were 13,495 insects collected in total from 300 pitfall and sweep net samples. Ninety samples were collected from 45 sites in the 2021 field season, and 210 samples were collected from 105 sites in the 2022 field season. From a total of 150 field sites, there were 4221 caterpillars, 120 weevils, 592 Lygus ssp., 2919 leafhoppers and 5643 flea beetles collected. All taxa showed significant differences in the amounts collected depending on sampling method with some taxa being collected more in sweep nets and others in pitfall traps. Tests of model assumptions using simulated residuals were non-significant (alpha = 0.05) for all taxa and prediction combinations. There was no evidence of residual spatial autocorrelation in any model for either trapping method using the Moran's I test.

Herbivore abundance in crop versus non-crop areas

The abundances of herbivores from all taxa were found to significantly differ between non-crop vegetation areas and the crop (Table 1). More caterpillars, Lygus ssp. and flea beetles were collected at the stations located within the crop, whereas more weevils and leafhoppers were collected at the stations located within the non-crop vegetation areas (Figure 2). These results were consistent across the two different methods of collection.

TABLE 1. Models testing prediction 1 (P1) showing that all taxa have significantly different mean abundances at the in-crop sites (SITE; where non-crop is the reference category), while controlling for other variables.

Term	Term type	Shrinkage	H_o interpretation	Leafhoppers		Weevil		Caterpillar		Lygus ssp.		Flea beetles
Term	Term type	Shrinkage	H_o interpretation	Estimate^a	p	Estimate^a	p	Estimate^a	p	Estimate^a	p	Estimate^a	p
INTERCEPT	Parametric	No	Intercept = 0	3.64	<0.01	0.04	0.88	−2.38	<0.01	−1.99	<0.01	1.48	<0.01
SITE (in crop)	Parametric	No	No effect	−2.11	<0.01	−1.51	<0.01	1.95	<0.01	1.10	<0.01	1.14	<0.01
METHOD (sweep net)	Parametric	No	No effect	−1.45	<0.01	−2.76	<0.01	1.98	<0.01	1.73	<0.01	−3.70	<0.01
FIELD A	Smooth	Yes	No spatial effect	0.74	0.21	1.82	0.08	0.00	0.58	2.11	0.01	2.71	<0.01
FIELD B	Smooth	Yes	No spatial effect	2.49	<0.01	1.46	0.17	1.92	0.02	1.19	0.22	0.00	0.88
FIELD C	Smooth	Yes	No spatial effect	0.00	0.48	1.60	0.02	1.46	0.28	1.54	0.16	1.39	0.05
FIELD D	Smooth	Yes	No spatial effect	0.00	0.64	0.00	0.39	0.00	0.87	0.00	0.71	0.35	0.38
FIELD E	Smooth	Yes	No spatial effect	0.00	0.91	0.00	0.80	0.00	0.99	0.00	1.00	2.50	0.01
FIELD F	Smooth	Yes	No spatial effect	1.87	0.05	2.34	0.02	0.00	0.59	0.55	0.20	3.76	<0.01
FIELD G	Smooth	Yes	No spatial effect	0.00	0.93	0.00	0.87	0.00	0.86	0.00	0.71	0.00	0.69
FIELD H	Smooth	Yes	No spatial effect	1.00	0.23	2.24	0.05	1.05	0.18	0.01	0.48	0.00	0.47
FIELD I	Smooth	Yes	No spatial effect	1.88	0.13	3.43	<0.01	2.79	0.08	3.26	0.03	2.05	0.11
FIELD J	Smooth	Yes	No spatial effect	2.23	0.02	1.18	0.11	0.00	0.97	0.15	0.36	2.93	0.01
AMONG FIELD	Random intercept	No	σ_intercept = 0	6.40	<0.01	6.56	<0.01	8.58	<0.01	8.19	<0.01	8.74	<0.01

Note: Null hypotheses (H_o interpretations) are given for each statistical test on the table. The method term is categorical, distinguishing between the different sampling methods. Bolded values indicate significance.
^a Estimates for parametric terms are coefficients, and for smooth and random intercept terms are effective degrees of freedom (EDF).

Herbivore abundance at different distances from the non-crop area

Leafhopper abundance was significantly and non-linearly associated with distance from non-crop area (Table S1). Visual interpretation of this smooth term demonstrates a negative relationship until about 200 m from the non-crop area (vertical line; Figure 3) after which the slope of the relationship approaches zero. Leafhopper abundance therefore drops on average as the distance from non-crop vegetation increases up to a point at which it levels off (Figure 3).

There was no evidence for a significant effect of distance from non-crop area for caterpillar, weevil, Lygus spp. and flea beetle abundance (Table S1).

Spatial effects

Although distance from non-crop area effects were not observed in four of the five taxa, all were observed to have modelled spatial effects across at least some of the fields in which they were sampled (Table S1). These ‘hot’ and ‘cold’ spots for abundance, unaligned with the distance from the non-crop area, are illustrated in Figures S1–S5.

DISCUSSION

This study explored the distributions of five herbivorous pest insect taxa to determine whether they were spilling into crop fields from non-crop vegetation areas during the late growing season. We predicted that more pest herbivores would be observed in the crop field relative to the adjacent non-crop vegetation area (P1), and that these taxa would not exhibit a decline in abundance at increasing distances from the non-crop vegetation areas (P2). Of the five taxa, weevils and leafhoppers did not meet these predictions, and only leafhoppers showed evidence of spilling into crops from non-crop areas during this time period.

Leafhopper abundance was found to be greater in the non-crop vegetation areas, and generally dropped in abundance at further distances from the non-crop areas, with the decline levelling-off towards the centre of the crop (Figure 2). These results are consistent with a spillover effect (i.e., non-crop areas, where leafhoppers are most abundant, serve as a source and insects dispersing from these reservoirs become less abundant at greater distances from this source). This result is aligned with previous findings that found leafhopper abundance to be greater in and near non-crop vegetation areas (Ribeiro et al., 2021). These results indicate that leafhoppers use non-crop vegetation differently than they use crop. Leafhoppers may prefer grasses as a host and sometimes spill into the crop to feed (Ribeiro et al., 2021). Some species of leafhoppers are vectors of aster yellows, a plant disease that alters plant physiology and reduces crop yield (Dara, 2017). However, less than 20% of leafhoppers are found to carry this disease, and the risk to canola crop is low (Dara, 2017; Soroka et al., 2015).

Weevils have consistent abundance at different distances from the non-crop vegetation areas, but were more abundant in the non-crop vegetation areas compared to within the crops. This might suggest that weevils prefer non-crop vegetation areas rather than canola as habitat and are not spilling into the crop. These results are consistent with Dosdall et al. (2006) study, which found that weevil abundance throughout canola fields is more even later in the growing season. The higher abundance of weevils in the non-crop areas could be due to different species of weevils present in the crop and non-crop areas utilizing these areas differently as demonstrated in Denys and Tscharntke's study (Denys & Tscharntke, 2002) found that insect diversity is greater in non-crop areas, such as field margins. However, it is also possible that spillover happened earlier in the growing season for weevils and that during the late season, the weevils have spread evenly throughout the crop. These findings could indicate that the crop is less of a draw for weevils late in the season as spillover is not ongoing even though weevils are more abundant in the non-crop areas.

Caterpillars, Lygus ssp. and flea beetles all had consistent abundances at different distances from the non-crop vegetation areas. They also had significantly higher abundance in the crop areas compared to the non-crop vegetation areas. This suggests no spillover effect for these taxa during the late season. Pests could spillover from neighbouring non-crop areas earlier in the season and then build-up their populations due to high foraging value of the crop. Also, differences in abundances between crop and non-crop area can be due to many landscape and management factors. Greater abundance in-crop suggests that the crops are providing all required resources rather than the non-crop vegetation areas during the late season. Lygus spp. and many caterpillars (e.g., bertha armyworms, Clover cutworms, diamondback moths) notably cause seed pod damage around the time of our sampling and therefore have reason to be in the crop feeding on seed pods at that time (Dosdall & Mason, 2010). However, flea beetles often do the most damage to fresh leaves in the early season and may not be as drawn into the crop during the late season by tough, mature leaves and seed pods (Dosdall & Mason, 2010; Gavloski et al., 2011). Flea beetles may be remaining in the crop from earlier spillover processes. Furthermore, other research shows that herbivorous insect distributions are not significantly impacted by the presence of non-crop vegetation in agricultural landscapes (Chaplin-Kramer et al., 2011) and insect distribution varies based on species and habitat (González et al., 2022; Karp et al., 2018). For example, previous studies did find a spillover effect for two canola pest species in these taxonomic groups. In these studies, tent caterpillars (Malacosoma americanum Fabricius) and L. lineolaris nymphs tended to aggregate closer to non-crop areas in fields (D'Ambrosio et al., 2019; Eckberg et al., 2015). These previous studies occurred in agroecosystems that were geographically remote from the Canadian Prairies or looked at large global trends (Karp et al., 2018; Severtson et al., 2015; Tonina et al., 2018). A regional lens is important to agricultural decision making as the overall system structure can vary greatly due to both geographical and agronomic factors. Species compositions can vary greatly between different regions, and different species have different functional behaviours and phenologies, which may impact when and how much pest pressure crops face. In-field insect distributions are also likely to differ due to the level of agricultural intensification (e.g., the Canadian Prairies have large fields when compared globally) and due to different environmental conditions, such as the plant species that provide alternate food sources for herbivorous insects (Barbercheck & Wallace, 2021). Large crop fields can create areas that some insects cannot access due to dispersal abilities and inhospitable management practices (Gardiner et al., 2010). It is also possible that pest taxa spillover into the crop at different times than when we collected samples. Caterpillars, Lygus ssp. and flea beetles may rely on non-crop vegetation areas at different times of the season, but during the late season, we find these pest taxa were not gaining measurable benefits from the non-crop vegetation.

All taxa demonstrated ‘hot’ and ‘cold’ spots in abundance that differed spatially among fields (Figure 1), which could be due to differences in the composition of the surrounding landscape in which each field is situated. The random differences between fields could also be explained by landscape variations but could also be due to insecticide treatments. Because we did not conduct collection in any unsprayed fields, we cannot know how insecticide treatment changed the dispersal of insects in the field. However, because treatments were applied across the whole field and earlier in the growing season, it is more likely that insecticide treatments may account for random differences between fields but have less impact on within field insect distributions. Because these treatments are commonly used, it is important to understand the spillover of pest insects under the pressures of insecticide treatment as we have examined them. Previous studies show that landscape composition has significant effects on both pest and natural enemy distributions (Karp et al., 2018; Schellhorn et al., 2014). Our results could indicate that both crop and non-crop land covers beyond the margins of the individual fields (not included in this analysis) are impacting insect distribution along with the land covers closely adjacent. This would be consistent with previous studies that have shown that surrounding vegetation and crop type impact insect distribution within fields (Goethe et al., 2021; McCabe et al., 2017).

This study explores distributions of five herbivorous pest insects of canola at a specific time in the growing season, several weeks before harvest. As such it represents a snapshot of herbivore distribution, at a time after which most of the yield-reducing damage to plants has likely occurred and seed pod damage may be occurring. It also considers only a subset of the pest taxa that may cause damage to crops. Other pest species present in this area include root maggots, aphids, various beetle species and thrips (Gavloski et al., 2011). Therefore, future research should evaluate the degree to which non-crop vegetation as a source for spillover may differ at other times of the season. For example, we might expect pest distributions to vary throughout the growing season, depending on the taxon, with factors such as life history and voltinism influencing distributional change (Tscharntke, Rand, & Bianchi, 2005; Wissinger, 1997). The ways in which the insect taxa interact with landscape could also change throughout the growing season, resulting in within-field migrations throughout the season. An example of this is found in a species of weevil (C. obstrictus) that aggregates closer to non-crop areas at the beginning stages of crop growth (Dosdall et al., 2006).

CONCLUSIONS

In this study, we determined that four of the five pest taxa we collected do not show evidence of spilling into the crop from the non-crop areas during the late growing season for an important crop in the Canadian Prairies. Other crops that are impacted by pest insects in this study system are barley and wheat (Alberta Parks, 2015; Bajwa et al., 2020; Starks & Webster, 1985). Some of the taxa included in this study are also pest insects to these other important crops (Bajwa et al., 2020; Starks & Webster, 1985). By understanding herbivore pest distributions in canola, we can begin to understand how pests may distribute in other crops that are also damaged by these insect taxa. Further research is needed to understand if these pests are spilling into other crop types in this and other study systems.

Only a single insect pest, representing a small risk for the crop, showed evidence of spillover into crops from non-crop area during the late season, suggesting that the late season is unlikely to be a time when non-crop vegetation drives pest pressure on canola. Our findings contribute to an evidence base that argues retaining non-crop vegetation within crop systems will not only be beneficial in supporting the biodiversity in these landscapes (Benton et al., 2003; Estrada-Carmona et al., 2022; Vickruck et al., 2021) but could be economically beneficial to growers (Clare et al., 2021; Galpern et al., 2020; Nelson et al., 2022). Non-crop areas have been found to host a variety of beneficial insects and arthropods (Bianchi et al., 2006; Galpern et al., 2021; Robinson et al., 2021; Vickruck et al., 2019), some of which act as natural enemies by preying on these pests (Bianchi et al., 2006). By maintaining non-crop vegetation areas supportive of natural enemies, growers may be able to rely less on costly insecticides to control pest insects. Removal of non-crop areas has been found to increase the use of insecticides (Malaj & Morrissey, 2022) resulting in a greater cost to growers. Non-crop vegetation areas have been shown to provide other ecosystem services, such as filtering water, allowing nutrient flow and climate regulation (Galpern & Gavin, 2020; Mitchell et al., 2014). Previous studies in the Canadian Prairies have found that retaining and restoring non-crop areas in these landscapes increases various desired benefits such as pollination (Purvis et al., 2020; Vickruck et al., 2021). The removal of non-crop areas has been found to potentially result in financial loss (Clare et al., 2021) and reduced crop yield (Nelson et al., 2022). Our study therefore contributes to a body of work demonstrating the low risk to crop growers of retaining non-crop vegetation in or near their fields.

AUTHOR CONTRIBUTIONS

Rebecca Innes: Data curation; investigation; writing – original draft. Tobyn Neame: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; writing – review and editing. Paul Galpern: Conceptualization; formal analysis; supervision; writing – review and editing.

ACKNOWLEDGEMENTS

We would like to thank the growers who allowed us access to their land for this study. This study would not have been possible without the help of Kiarra Brill, Sylvia Neumann, Farah Kandil, Jacinta Correch and Emma Pedersen who assisted with field work. We would like to thank Dr. Abigail Cohen for her assistance in editing, as well as Dr. Mindi Summers and Dr. Sam Robinson for assisting in developing this project. We would also like to thank the other members of the Agriculture, Biodiversity, and Conservation Lab for their feedback. Funding for this work was provided by the Canola Council of Canada, the Alberta Canola Producers Commission and the Manitoba Canola Growers.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Open Research

DATA AVAILABILITY STATEMENT

Data available on request from the authors.

Supporting Information

Filename

Description

afe12626-sup-0001-Figures.docxWord 2007 document , 1.2 MB

Figure S1. Leafhopper within-field spatial maps, showing ‘hot’ (yellow) and ‘cold’ (blue) spots in abundance in those fields where these terms were significant (p < 0.05). Effect is measured on the log scale of the linear predictor. This within-field variability was controlled for by the model when estimating crop and non-crop mean abundances for this taxon. Field maps are shown at approximately the same spatial scale.

Figure S2. Weevil within-field spatial maps, showing ‘hot’ (yellow) and ‘cold’ (blue) spots in abundance in those fields where these terms were significant (p < 0.05). Effect is measured on the log scale of the linear predictor. This within-field variability was controlled for by the model when estimating crop and non-crop mean abundances for this taxon. Field maps are shown at approximately the same spatial scale.

Figure S3. Flea beetle within-field spatial maps, showing ‘hot’ (yellow) and ‘cold’ (blue) spots in abundance in those fields where these terms were significant (p < 0.05). Effect is measured on the log scale of the linear predictor. This within-field variability was controlled for by the model when estimating crop and non-crop mean abundances for this taxon. Field maps are shown at approximately the same spatial scale.

Figure S4. Armyworm within-field spatial maps, showing ‘hot’ (yellow) and ‘cold’ (blue) spots in abundance in those fields where these terms were significant (p < 0.05). Effect is measured on the log scale of the linear predictor. This within-field variability was controlled for by the model when estimating crop and non-crop mean abundances for this taxon. Field maps are shown at approximately the same spatial scale.

Figure S5. Lygus bug within-field spatial maps, showing ‘hot’ (yellow) and ‘cold’ (blue) spots in abundance in those fields where these terms were significant (p < 0.05). Effect is measured on the log scale of the linear predictor. This within-field variability was controlled for by the model when estimating crop and non-crop mean abundances for this taxon. Field maps are shown at approximately the same spatial scale.

afe12626-sup-0002-Tables.docxWord 2007 document , 19.4 KB

Table S1. Models testing prediction 1 (P1) while controlling for other variables. Null hypotheses (Ho interpretations) are given for each statistical test on the table.

Table S2. Percent deviance in abundance explained by each of the 10 models used in this study.

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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Volume26, Issue3

August 2024

Pages 421-431

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RES Journal Early Career Entomologist Awards 2024

Contrasting late season pest insect abundance in non-crop vegetation areas and nearby canola fields in the Canadian Prairies

Abstract

INTRODUCTION