Edge-biased distributions of insects. A review
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Spatial ecology includes research into factors responsible for observed distribution patterns of organisms. Moreover, the spatial distribution of an animal at a given spatial scale and in a given landscape may provide valuable insight into its host preference, fitness, evolutionary adaptation potential, and response to resource limitations. In agro-ecology, in-depth understanding of spatial distributions of insects is of particular importance when the goals are to (1) promote establishment and persistence of certain food webs, (2) maximize performance of pollinators and natural enemies, and (3) develop precision-targeted monitoring and detection of emerging outbreaks of herbivorous pests. In this article, we review and discuss a spatial phenomenon that is widespread among insect species across agricultural systems and across spatial scales—they tend to show “edge-biased distributions” (spatial distribution patterns show distinct “edge effects”). In the conservation and biodiversity literature, this phenomenon has been studied and reviewed intensively in the context of how landscape fragmentation affects species diversity. However, possible explanations of, and also implications of, edge-biased distributions of insects in agricultural systems have not received the same attention. Our review suggests that (1) mathematical modeling approaches can partially explain edge-biased distributions and (2) abiotic factors, crop vegetation traits, and environmental parameters are factors that are likely responsible for this phenomenon. However, we argue that more research, especially experimental research, is needed to increase the current understanding of how and why edge-biased distributions of insects are so widespread. We argue that the fact that many insect pests show edge-biased distribution patterns may be used to optimize both pest monitoring practices and precision targeting of pesticide applications and releases of natural enemies.
KeywordsSpatial distribution Insect sampling Pest management Precision agriculture
In addition to a rich body of literature on relationships between biodiversity and ecological or environmental edges in natural and semi-natural ecosystems, there are numerous reports of similar edge-biased distributions of insects in agricultural systems. However, this specific topic has never been reviewed, and it has not been emphasized how this unique aspect of spatial distribution patterns is highly relevant to both insect monitoring and pest management in agricultural systems. Here, we describe published reports of insect distributions with edge-biased distributions across a wide range of agricultural systems. We discuss the following two questions: (1) What are the potential mechanisms responsible for an edge-biased distribution of insect populations in agricultural systems? (2) How can increased knowledge about edge-biased distributions and their governing mechanisms help to enhance the efficiency of agricultural pest management practices?
2 Edge-biased distributions in agricultural systems
The following sections are not meant to provide exhaustive reviews but aim to provide an insight into the prevalence of edge-biased insect distributions in a range of agricultural systems and across a wide range of spatial scales.
2.1 Edge-biased distributions in open field systems
An edge-biased distribution of insects in agricultural systems was first reported in 1950 in a study on black bean aphids, Aphis fabae Scop. (Hemiptera: Aphididae) on twigs of Euonymus europaeus L. (Celastrales: Celastraceae) and Viburnum opulus L. (Dipsacales: Adoxaceae) (Johnson 1950). The author noted that the onset of infestations by black bean aphids was near field edges and eventually spread throughout the field, although field edges would be the areas with the highest aphid density. Winder et al. (1999) conducted a large-scale study of the effect of grid size on the analysis of the spatial distribution of English grain aphids, Sitobion avenae Fabricius (Hemiptera: Aphididae) and found that significant edge effect was detected for large grid size with higher count of aphids along field edges. However, the edge-biased distribution was not evident at small grid sizes. Thus, sampling unit size was highlighted as a critical aspect of the ability to detect and quantify spatial trends, such as edge-biased distributions. Another large-scale spatial distribution study of common farmland arthropods in winter wheat fields also demonstrated higher density of species along edges (Holland et al. 1999). The studies by Winder et al. (1999) and Holland et al. (1999) noted the effervescent occurrence of edge-biased distributions. These findings agree with Wilson and Morton (1993) who showed that edge-biased distribution of two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae), in cotton was only prominent early in the growing season but eventually diminished as the season progressed. Studying the spatial distribution of cabbage aphids, Brevicoryne brassicae L. (Hemiptera: Aphididae) in commercial canola field sites, Severtson et al. (2015) also found significantly higher counts of this important pest within 20–30 m from field edges compared to further inwards into canola fields. Based on logistic regression, the population density of cabbage aphids was found to be negatively correlated with the distance from field edges in dryland canola host plants. Besides aphids, cabbage seed weevils, Ceutorhynchus assimilis Paykull (Coleoptera: Curculionidae), displayed a similar spatiotemporal pattern of distribution in winter oilseed rape (Ferguson et al. 2000). A study describing the distribution of Andean potato weevils, Premnotrypes spp. (Coleoptera: Curculionidae), showed heavier infestation of potato tubers along field edges (Parsa et al. 2012). Edge-biased distribution was also reported regarding wheat stem sawflies, Cephus cinctus Norton (Hymenoptera: Cephidae), in dryland wheat fields (Nansen et al. 2005a, 2005b).
2.2 Edge-biased distributions in orchard systems
The distribution of wild bees in high bush blueberry, Vaccinium corymbosum L. (Ericales: Ericaceae), orchards showed significantly higher abundance of bees [Ceratina calcarata Robertson (Hymenoptera: Apoidea), Ceratina dupla Say (Hymenoptera: Apoidea), Augochlorella aurata Smith (Hymenoptera: Apoidea), and Lasioglossum pilosum Smith (Hymenoptera: Apoidea)], especially along the edges of orchards as compared to the interior (Tuell et al. 2009). In addition, it was found that distance to orchard edge was an important predictor of abundance of red mason bees, Osmia bicornis L. (Hymenoptera: Megachilidae), in apple orchards (Gruber et al. 2011). Higher bee activity and greater preference for nesting of bees were noted along orchard edges compared to orchard interior. Such distribution pattern, especially that of O. lignaria, was linked to the presence of big leaf lupine, Lupinus polyphyllus Lindley (Fabales: Fabaceae), planted along field edges (Sheffield et al. 2012). In addition, a study of the spatial distribution of Asian citrus psyllids, Diaphorina citri Kuwayama (Hemiptera: Liviidae), in citrus groves (grapefruit and orange) showed strong season-long edge-biased distributions (Setamou and Bartels 2015).
2.3 Edge-biased distributions in stored product systems
In the context of stored products, warehouse walls can be considered as physical or environmental edges. Insects associated with stored products have been shown to aggregate along warehouse walls and near physical edges. A study of the spatial distribution of red flour beetles, Tribolium castaneum Herbst (Coleoptera: Tenebrionidae), in stored wheat warehouse showed consistent aggregations along warehouse walls (Athanassiou et al. 2005). This wall-biased distribution has also been observed regarding other stored product pests, such as Trogoderma variabile Ballion (Coleoptera: Dermestidae) (Campbell et al. 2002). Similarly, a study of the distribution of insect pests in a botanical warehouse in Florida found higher abundance of both moths and beetles [Cadra cautella Walker (Lepidoptera: Pyralidae), Lasioderma serricorne Fabricius (Coleoptera: Anobiidae), Oryzaephilus mercator Fauvel (Coleoptera: Silvanidae), Typhaea stercorea L. (Coleoptera: Mycetophagidae), and Indianmeal moths, Plodia interpunctella Hübner (Lepidoptera: Pyralidae)] along the walls and corners of the warehouse (Arbogast et al. 2002). In an experimental study with pheromone traps placed in 1-m intervals along a vertical gradient from the floor level to the ceiling in a 6-m tall warehouse, most Indian meal moths were caught predominantly either near the ground level or near the ceiling (Nansen et al. 2004), thus showing a vertical edge-biased distribution. Interestingly, the same study showed that the moths’ preference for vertical edges could be eliminated through addition of landing surfaces to pheromone traps or by placing traps near walls. Thus, the study by Nansen et al. (2004) showed important ways to enhance moth trap captures through a combination of manipulation (addition of landing platform) and targeted placements of traps near walls, and both improvements hinged on the importance of recognizing edge bias of moth captures.
2.4 Edge-biased distributions in small-scaled laboratory experiments
Several small-scale laboratory studies have indicated the occurrence of edge-biased distributions of insects. As an example, female almond moths, C. cautella, were released into a 120-cm-square arena with uniform layers of intact or cracked peanuts, and the spatial distribution of eggs laid by these female was recorded after 48 h (Mankin et al. 2014). The results indicated that when peanuts filled the entire arena, female almond moths significantly aggregated their oviposition along arena edges. Moreover, when only the central 25% of the arena was filled with peanuts, the majority of the eggs were found along the edges of the peanut patches instead of the edge of the arena. Similarly, the infestation pattern of the red flour beetles, T. castaneum, also showed insect aggregations along arena edges (Campbell and Hagstrum 2002).
3 Theoretical models of edge-biased insect distributions
Given the prevalence of edge-biased distributions of insects in agricultural systems, it seems of considerable importance to unravel the driving mechanisms of the phenomenon. One of the common approaches used in the study of edge-biased distributions is the development of models that can simulate the edged-biased pattern of insect distributions. Although these models are theoretical and based on assumptions that are difficult to validate, they have been used widely on a conceptual basis and also to produce meaningful predictions of insect distributions.
3.1 Simple diffusion model
3.2 Stratified diffusion model
Despite support for the simple diffusion model, this model does not explain the initial aggregation point and therefore what is driving the establishment of insect populations near edges. In addition, Liebhold and Tobin (2008) argue that the relevance of simple diffusion models to predict spatial distributions of insects is limited to small spatial scales and fails to account for greater rate of diffusion at large spatial scales. In addition, large-scale insect distributions show a certain degree of aggregation and patchiness that varies over time (Winder et al. 1999; Holland et al. 1999; Dean 1973). To account for the patchiness of insect distributions and the faster speed of insect dispersal at a large spatial scale, Liebhold and Tobin (2008) propose stratified diffusion model to consider the effects of stochastic events, such as accidental spread of insects due to human activities and prevailing wind (Helson 1958). That means there will be groups of insect colonies leaping ahead of the main population front line. Each of these new groups become new centers of dispersal. Although stratified diffusion model has been shown to improve predictions of insect distributions compared to simple diffusion models, they are also known to have limitations. Stratified diffusion models, just like simple diffusion models, rely on the concept of insect population density over distance as a time-dependent function. As time elapses, insects will eventually spread randomly and evenly across crop fields with little population density variance, failing to account for the aggregation and patchiness in insect distribution in many agricultural systems in the long term.
3.3 Influential edge models
4 Potential driving factors of edge-biased distributions
In light of an attractive edge as a potential explanation for the occurrence of edge-biased insect distributions in agricultural systems, the following question needs to be discussed: What set(s) of conditions would make an agricultural edge attractive? Before further discussion of this question, it is worth mentioning that the term “attractive edge,” as described by Olson and Andow (2008), should be broadly understood as an edge environment that leads to higher insect density than further inwards into agricultural systems. Therefore, an attractive edge does not necessarily act as favorable environment that attracts insects, but it can also or simply act as a trapping environment that reduces insects’ dispersal.
4.1 Mobility-altering factors
4.1.1 Wind patterns
Several studies have examined effects of the combination of natural landscape fragmentation and microclimatic conditions and how such variables affect insect communities in non-agricultural habitats (Stangler et al. 2015). The spatial distribution of a butterfly, Lopinga achine Scopoli (Lepidoptera: Nymphalidae), showed distinct edge-biased distribution, which was explained by specific microclimatic conditions leading to the ground cover of Carex montana L. (Cyperaceae) having the highest abundance near forest edges (Bergman 1999). In a study of bark beetle [Ips typographus L. (Coleoptera: Curculionidae)] infestations, Kautz et al. (2013) investigated the risk of infestation in three types of forest edges and compared the results of trees from the interior of the same forests. They concluded that the risk of bark beetle infestation was highest in forest patches cleared by sanitary logging measures, in particular along south-facing edges. Thus, the combination of a particular edge management practice and ambient temperature was highlighted as a key driver of bark beetle infestations. Although exclusively based on analyses of data derived from natural habitats, such studies strongly suggest that edge-biased distributions are often explained by microclimatic differences between edge and interior locations. Using different types environmental sensors, it has been demonstrated how abiotic variables, including ground surface temperature and soil temperature, show considerable with-field variation and in particularly vary between edges and inwards into fields (Bense et al. 2016). Such microclimatic variation can lead to spatial variation in crop growth and relative suitability of host plants and therefore lead to edge-biased distribution of herbivorous insects in agricultural systems.
4.1.3 Terrain property
Although windbreaks and hedgerows are common features in many agricultural systems, sole reliance on the shelter effect and microclimate promoted by these features does not provide encompassing account for the occurrence of edge-biased distributions, especially for agricultural systems in which they are not present. Other factors that are more intrinsic to the agricultural environment, such as the nature of landscape matrix, have been shown to affect insect rate of dispersal and emigration and result in their aggregation along edges. In a marked release and re-captured experiment on movement of delphacid planthopper, Prokelisia crocea Van Duzee (Hemiptera: Delphacidae), in patches of prairie cordgrass, the rate of emigration from cordgrass patches bordered with brome grass matrix was higher than that from cordgrass patches bordered with mudflat (Haynes and Cronin 2003). The same study also noted the accumulation of planthoppers along cordgrass-mudflat edges, but no such aggregation of planthoppers was observed along cordgrass-brome edges. It was later confirmed that the lower permeability of cordgrass-mudflat edges makes this type of edges a barrier to emigration since planthoppers behaviorally avoid crossing highly contrast border between vegetated cordgrass matrix to sparsely vegetated mudflat matrix (Haynes and Cronin 2006). Moreover, the cordgrass-mudflat border was clearly not a reflective edge, as planthoppers did not show edge-avoidance behavior. Meanwhile, the distribution of planthoppers within a homogenous patch of cordgrass was found to be random (Cronin 2003). Therefore, although landscape matrix permeability may explain why insects can be contained in their original patches, it does not directly explain the aggregation of insects along edges, especially for the case of delphacid planthoppers.
4.2 Vegetational factors contributing to edge-biased distributions
4.2.1 Vegetation heterogeneity
In cases where agricultural systems are adjacent to natural forests, the edges can be described as an “ecotone” that marks the ecological change between two environments (Marshall and Moonen 2002). The adjacency of vegetation of two different habitats (agricultural fields and natural forests) results in an increase in floral diversity and vegetation heterogeneity (Marshall 1989; Yahner 1988). The increase in heterogeneity of vegetation along field edges has been shown to increase carabid species richness along field edges (Magura 2002). For example, boreal forests with sparse ground vegetation were found to have lower numbers of herbivorous invertebrate prey for carabids (Niemelä and Spence 1994). The interspersion between grasses, shrubs, and boreal trees as seen in the edges between farming lands and boreal forests can effectively increase the density of ground vegetation, resulting in greater abundance of herbivorous invertebrates which, in turn, leads to higher carabid abundance along edges (Magura 2002). Similarly, the interspersion of vegetation types between the forest edges and urban habitat in north Ohio has been attributed to the increase in ant (Hymenoptera: Formicidae) species richness (Ivanov and Keiper 2010). Therefore, the ecotone environment observed along field edges could serve as a preferential habitat for some insect species.
4.2.2 Plant quality
In the discussion of edge-biased distributions in relation to the vegetation heterogeneity, there seems to be an underlying assumption that vegetation homogeneity exists within a given habitat or agricultural system. Therefore, the heterogeneity of vegetation observed in the ecotone along edges is mainly caused by the interspersion between the vegetation types of two environments, while the within-system variation of vegetation is often ignored. However, despite cropping systems predominantly being monocultures and under a quasi-homogeneous management regime, there is broad evidence of significant vegetational heterogeneity within agricultural fields. That means individual crop plants within an agricultural field may have differential growth rates, phenotypes, and quality. This issue was highlighted above as microclimatic conditions may vary within fields and show distinct gradients between edges and locations further inwards into fields (Bense et al. 2016). Edge-biased crop development has been observed and reported for almost 100 years in winter wheat fields, where wheat rows along the borders showed higher yields than those in the center (Arny 1922). Over the years, rice (Wang et al. 2013), maize and climbing beans (Davis et al. 2008; Tiegu et al. 2012), wheat (Wu and Li 2002; Bulinski and Niemczyk 2015), millets, Sudan grass (Drapala and Johnson 1961), soybean (Teng et al. 2008), cotton (Luckett et al. 1992), rapeseed (Buliński and Niemczyk 2010), carrots, cabbages, and onions (Peach et al. 2000) have been shown to display edge-biased distribution of growth. In rice, the increase in yield of border rows compared to central portions of fields or plots ranged from 63 to 68% (Bulinski and Niemczyk 2015). The difference can be as large as 394% between edge and center crops (Hadjichristodoulou 1993). The major factors accounting for such edge-biased growth difference have been attributed to competition for nutrients and light among crop plants (Peach et al. 2000).
The body of research documenting edge-biased distributions of crop development often regards the phenomenon as undesirable and seeks ways to curb the effect to avoid over-estimation in yield calculations (Wang et al. 2013; Miller and Mountier 1955; Arny 1922). However, from an entomological perspective, the research focus would be to study how within-field vegetational heterogeneity contributes to the edge-biased distribution of insects in agricultural systems. We are unaware of any published study addressing this question directly, but the edge resource model by Ries and Sisk (2004) could partially explain this, as microclimatic conditions near edges may affect host plant growth and quality (suitability as a host plant for insect herbivores). Nonetheless, given that edge crops can have significantly higher yield than center crops, it is reasonable to hypothesize that there are physiological, and therefore nutritional, differences between edge and center crops. If such physiological differences are linked to differences in suitability as hosts and insects are able to—via volatiles or visual cues—perceive such differences among crop plants, then vegetational heterogeneity may partially explain observed edge-biased distributions of insects. That is, vegetational heterogeneity may drive selection by favoring both behavioral attraction and oviposition behavior by insects to certain plants. In addition, difference in suitability of crop plants will likely lead to differential growth and survival rates of insects. As an example, longevity and fecundity of Sitobion avenae and Rhopalosiphum padi L. (Hemiptera: Aphididae) have been shown to increase with the level of nitrogen fertilizer applied to wheat host plants (Aqueel and Leather 2011). Meanwhile, poor-quality host plants can cause many female insects to resorb their own eggs to mobilize nutrients for their survival (Awmack and Leather 2002). In broader term, female insects have been shown to vary their oviposition and number of eggs laid in accordance to the suitability of host plants for the performance of future progeny (Gripenberg et al. 2010). Therefore, the better-quality edge crops may be preferred by insects or provide better growing conditions for insects and their larval stages, resulting in a skewed distribution towards crop edges. However, using plant vigor to explain edge-biased distribution of insects may also involve important trade-offs. On one hand, more vigorous plants may provide insects and their larvae with more nutrients for their development. On the other hand, healthier plants can also produce more defensive chemicals such as alkaloids, glucosinolates, and phenolics to protect themselves from insect herbivores (Awmack and Leather 2002). Furthermore, the idea of plant quality as potential explanation for insect aggregation along edges is also contested by the data reported by Haynes and Cronin (2003). Their study found that the nitrogen content of plants along mudflat-cordgrass edges and brome-cordgrass edges was both higher than that of plants in the interior. Interestingly, delphacid planthoppers aggregated along mudflat-cordgrass edges but not along brome-cordgrass edges, implying that host plant quality may not be the main driving factor of edge effect. Thus, we argue that considerable research is needed to investigate the complex of factors affecting plant growth along edges compared to inwards into cropping systems and how differential plant growth may affect their role as suitable hosts for associated insect communities.
5 Potential applications of edge-biased distributions into insect monitoring and pest management
Originally, skewed distributions of agricultural insects towards field edges were mostly regarded as a source of error when developing insect sampling plans. Therefore, the data collected along field edges were not considered representative of the actual insect population (Fleischer et al. 1999). However, edge-biased distributions of insects in agricultural systems can potentially provide opportunities to optimize sampling efforts. Currently, sampling units employed in sampling plans are often randomized and spread across the field. Such an approach can be highly labor intensive. Given the prevalence of edge effects in many insect species, stratified sampling plans with a greater focus along field edges than field interiors can reduce sampling efforts as well as increase sampling accuracy if a stable spatial pattern of insect distribution within an agricultural system is well studied and established. For example, increased performance (less sampling needed) of such a spatially targeted sampling approach was demonstrated for the wheat stem sawfly (Nansen et al. 2012). In addition, Severtson et al. (2016) described a spatially optimized sequential sampling plan for cabbage aphids in canola fields. In their study, canola fields were divided into two types of sampling grids: inner grids and edge grids. Edge grids were defined as the areas within 20 m of the field edge, while inner grids were the rest of the sampling grids. Their sampling results showed that 9 out of 20 edge grids displayed infestation level above threshold while only 2 out of 20 inner grids showed infestation level above threshold. Taking the proposed edge effect distribution of aphids into account, Severtson et al. (2016) conducted stratified sampling analysis and managed to reduce spatial variability as well as to increase accuracy of infestation level. Nonetheless, we also acknowledge that edge effect distribution of insects in many agricultural systems can be highly seasonal and temporally dependent (Corbett and Rosenheim 1996; Winder et al. 1999; Holland et al. 1999; Wilson and Morton 1993). Furthermore, applications of insecticides in agricultural fields have been proven to greatly change the edge-biased distribution of insects to random distribution across agricultural fields (Trumble 1985; De Jiu and Waage 1990; Maredia et al. 2003). Therefore, the mentioned limitations may jeopardize the robustness of edge-stratified sampling methods and highlight the importance of timing in proposed edge-stratified sampling methods.
Broadly speaking, increased understanding of the mechanisms responsible for edge-biased distributions can be used to improve the practices of precision agriculture. As compared to conventional agriculture, precision agriculture focuses more on timely and targeted application of treatments to control insect pest infestations rather than field-wide and calendar-based spraying of chemicals (Council 1997). The knowledge of edge-biased distributions can help create targeted sampling plans to generate more reliable mapping of insect distributions and enable more targeted application of pesticides used in IPM programs (Weisz et al. 1995). Targeted application of insecticide has the potential to reduce pesticide usage in conventional farming systems yet maintain an effective control over agricultural pests. Focusing spray along field borders can also potentially address the issue of spray coverage, one of the main problems faced in field-wide treatment of pesticides that severely reduce the efficacy of applied chemicals (Nansen and Ridsdill-Smith 2013). An example of successful targeted application of insecticides would be the recent efforts to control brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), in Northeastern USA by focus spraying along edges in peach orchards instead of orchard-wide application (Blaauw et al. 2015). The results are promising as pesticide usage was reduced by 25–61% using this method and the damage on peach caused by brown mamorated stink bugs was also reduced when compared with the common standard practices. In addition, better understanding of edge-biased distributions may be used to optimize deployments of anti-insect nets in the orchard industry (Castellano et al. 2008). Finally, there is a growing body of literature on the potential use of banker plant systems (Zheng et al. 2017; Gurr et al. 2004, 2012, 2015, 2016; Lu et al. 2015; Zehnder et al. 2007; Hossain et al. 2002). In these agricultural systems, the agro-ecological landscape is manipulated through integration of special plants along crop banks. These banker plants serve several purposes, including (1) nectar and pollen for natural enemies, (2) host plants for alternative prey of natural enemies, and (3) shelter and oviposition sites for natural enemies. Thus, banker plant systems represent ways to enhance the performance of biological control of crop pest. A very important aspect of banker plant systems is that the landscape manipulation is conducted along field edges, so edge-biased distributions of many insect pests are a contributing factor to the growing interest in this type of agricultural systems.
As discussed in this review, edge-biased insect distributions represent a significant phenomenon that is observed in both natural and agricultural systems and across a wide range of spatial scales. Although there have been very important modeling studies attempting to explain edge-biased distributions (Ries and Sisk 2004; Ries et al. 2004), the phenomenon has not been studied extensively on the basis of manipulative/experimental research, and we wish to highlight this important research gap. Agricultural systems lend themselves nicely to such manipulative/experimental research, so we argue that the broader community of landscape ecologists should consider agricultural systems when testing their theories on how biodiversity is affected by landscape fragmentation and environmental edges. Improved knowledge and appreciation of edge-biased distributions can greatly facilitate the development and implementation of precision agriculture, such as optimization of sampling efforts and creation of more accurate insect distribution maps that enable targeted application of treatments. Finally, edge-biased distributions of insects are one of the underlying reasons why ecological engineering (Gurr et al. 2012, 2015) and banker plant systems are being recognized as sustainable alternatives to a growing number of conventional farming systems.
The authors are grateful to Prof. Jay Rosenheim who provided an invaluable review of this article.
- Arny A (1922) Border effect and ways of avoiding it. Agron JGoogle Scholar
- Awmack CS, Leather SR (2002) Host plant quality and fecundity in herbivorous insects. Annu Rev Entomol 47(1):817–844. https://doi.org/10.1146/annurev.ento.47.091201.145300 CrossRefPubMedGoogle Scholar
- Bulinski J, Niemczyk H (2010) Edge effect in winter rape cultivation technology with traffic paths. EDITORIAL BOARD:5Google Scholar
- Bulinski J, Niemczyk H (2015) Assessment of border effect in wheat cultivation with tramlines. Annals of Warsaw University of Life Sciences-SGGW Agriculture (65 Agric. Forest Eng)Google Scholar
- Corbett A, Rosenheim JA (1996) Impact of a natural enemy overwintering refuge and its interaction with the surrounding landscape. Ecol Entomol 21(2):155–164. https://doi.org/10.1111/j.1365-2311.1996.tb01182.x CrossRefGoogle Scholar
- Council NR (1997) Precision agriculture in the 21st century: geospatial and information technologies in crop management. Natl Academy Pr. doi: https://doi.org/10.17226/5491
- Cronin JT (2003) Movement and spatial population structure of a prairie planthopper. Ecology 84(5):1179–1188. https://doi.org/10.1890/0012-9658(2003)084[1179:MASPSO]2.0.CO;2Google Scholar
- Dean BG (1973) Aphid colonization of spring cereals. Ann Appl Biol 75(2):183–193. https://doi.org/10.1111/j.1744-7348.1973.tb07298.x CrossRefGoogle Scholar
- Didham R (1997) The influence of edge effects and forest fragmentation on leaf litter invertebrates in central Amazonia. Tropical forest remnants: ecology, management, and conservation of fragmented communities. University of Chicago Press, Chicago, pp 55–70Google Scholar
- Drapala W, Johnson CM (1961) Border and competition effects in millet and sudangrass plots characterized by different levels of nitrogen fertilization. Agron J 53(1):17–19. https://doi.org/10.2134/agronj1961.00021962005300010006x CrossRefGoogle Scholar
- Ferguson AW, Klukowski Z, Walczak B, Perry JN, Mugglestone MA, Clark SJ, Williams IH (2000) The spatio-temporal distribution of adult Ceutorhynchus assimilis in a crop of winter oilseed rape in relation to the distribution of their larvae and that of the parasitoid Trichomalus perfectus. Entomol Exp Appl 95(2):161–171. https://doi.org/10.1046/j.1570-7458.2000.00654.x CrossRefGoogle Scholar
- Gurr GM, Heong KL, Cheng JA, Catindig J (2012) Ecological engineering strategies to manage insect pests in rice. In: Gurr GM, Wratten SD, Snyder WE, Read DMY (eds) Biodiversity and insect pests: key issues for sustainable management. Wiley-Blackwell, Chichester, pp 214–229. https://doi.org/10.1002/9781118231838.ch13 CrossRefGoogle Scholar
- Gurr GM, Lu Z, Zheng X, Xu H, Zhu P, Chen G, Yao X, Cheng J, Zhu Z, Catindig JL, Villareal S, Van Chien H, Cuong LQ, Channoo C, Chengwattana N, Lan LP, Hai LH, Chaiwong J, Nicol HI, Perovic DJ, Wratten SD, Heong KL (2016) Multi-country evidence that crop diversification promotes ecological intensification of agriculture. Nat Plants 2(3):16014. https://doi.org/10.1038/nplants.2016.14 http://www.nature.com/articles/nplants201614#supplementary-information CrossRefPubMedGoogle Scholar
- Gurr GM, Scarratt SL, Wratten SD, Berndt L, Irvin N (2004) Ecological engineering, habitat manipulation and pest management. In: Gurr GM, Wratten SD, Alteiri MA (eds). CSIRO Publishing, Collingwood, AustraliaGoogle Scholar
- Gurr GM, Zhu ZR, You MS (2015) The big picture: prospects for ecological engineering to guide the delivery of ecosystem services in global agriculture. In: Heong KL, Cheng J, Escalada MM (eds) Rice planthoppers: ecology, management, socio economics and policy Springer Science and Business Media. Netherlands, Dordrecht, pp 143–160Google Scholar
- Harris LD (1988) Edge effects and conservation of biotic diversity. Conserv Biol 2(4):330–332. https://doi.org/10.1111/j.1523-1739.1988.tb00196.x CrossRefGoogle Scholar
- Johnson C (1950) Infestation of a bean field by Aphis fabae Scop. in relation to wind direction. Ann Appl Biol 37(3):441–450. https://doi.org/10.1111/j.1744-7348.1950.tb00967.x CrossRefGoogle Scholar
- Julião G, Amaral M, Fernandes G, Oliveira E (2004) Edge effect and species–area relationships in the gall-forming insect fauna of natural forest patches in the Brazilian Pantanal. Biodivers Conserv 13(11):2055–2066. https://doi.org/10.1023/B:BIOC.0000040006.81958.f2 CrossRefGoogle Scholar
- Lewis T (1965a) The effects of an artificial windbreak on the aerial distribution of flying insects. Ann Appl Biol 55(3):503–512. https://doi.org/10.1111/j.1744-7348.1965.tb07963.x CrossRefGoogle Scholar
- Lewis T (1965b) The effect of an artificial windbreak on the distribution of aphids in a lettuce crop. Ann Appl Biol 55(3):513–518. https://doi.org/10.1111/j.1744-7348.1965.tb07964.x CrossRefGoogle Scholar
- Lewis T, Stephenson J (1966) The permeability of artificial windbreaks and the distribution of flying insects in the leeward sheltered zone. Ann Appl Biol 58(3):355–363. https://doi.org/10.1111/j.1744-7348.1966.tb04395.x CrossRefGoogle Scholar
- Liebhold AM, Tobin PC (2008) Population ecology of insect invasions and their management. Annu Rev Entomol 53(1):387–408. https://doi.org/10.1146/annurev.ento.52.110405.091401 CrossRefPubMedGoogle Scholar
- Lu ZX, Zhu PY, Gurr GM, Zheng XS, Chen GH, Heong KL (2015) Rice pest management by ecological engineering: a pioneering attempt in China. In: Heong KL, Cheng JA, Escalada MM (eds) Rice planthoppers: ecology, management, socio economics and policy. Springer Science and Business Media, Dordrecht, pp 163–180Google Scholar
- Maredia KM, Dakouo D, Mota-Sanchez D (2003) Integrated Pest Management in Mexico. In: Integrated pest management in the global arena. CABI, pp 273–284. doi: https://doi.org/10.1079/9780851996523.0000
- Miller J, Mountier N (1955) The border row effect in wheat trials with different spacings between plots. NZJ Sci Tech 37:287–299Google Scholar
- Murphy SM, Battocletti AH, Tinghitella RM, Wimp GM, Ries L (2016) Complex community and evolutionary responses to habitat fragmentation and habitat edges: what can we learn from insect science? Curr Opin Insect Scie 14(Supplement C):61–65. https://doi.org/10.1016/j.cois.2016.01.007 CrossRefGoogle Scholar
- Nansen C, Ridsdill-Smith JT (2013) The performance of insecticides-a critical review. INTECH Open Access Publisher doi: https://doi.org/10.5772/53987
- Niemelä JK, Spence JR (1994) Distribution of forest dwelling carabids (Coleoptera): spatial scale and the concept of communities. Ecography 17(2):166–175. https://doi.org/10.1111/j.1600-0587.1994.tb00090.x CrossRefGoogle Scholar
- Peach L, Benjamin LR, Mead A (2000) Effects on the growth of carrots (Daucus carota L.), cabbage (Brassica oleracea var. capitata L.) and onion (Allium cepa L.) of restricting the ability of the plants to intercept resources. J Exp Bot 51(344):605–615. https://doi.org/10.1093/jexbot/51.344.605 CrossRefPubMedGoogle Scholar
- Ries L, Fletcher RJ, Battin J, Sisk TD (2004) Ecological responses to habitat edges: mechanisms, models, and variability explained. Annu Rev Ecol Evol Syst 35(1):491–522. https://doi.org/10.1146/annurev.ecolsys.35.112202.130148 CrossRefGoogle Scholar
- Saunders DA, Hobbs RJ, Margules CR (1991) Biological consequences of ecosystem fragmentation: a review. Conserv Biol 5(1):18–32. https://doi.org/10.1111/j.1523-1739.1991.tb00384.x CrossRefGoogle Scholar
- Stangler ES, Hanson PE, Steffan-Dewenter I (2015) Interactive effects of habitat fragmentation and microclimate on trap-nesting Hymenoptera and their trophic interactions in small secondary rainforest remnants. Biodivers Conserv 24(3):563–577. https://doi.org/10.1007/s10531-014-0836-x CrossRefGoogle Scholar
- Teng W-l, Han Y-p, Li W-b (2008) Marginal effect index on the yield characters of soybean cultivars with different leaf shape [J]. Soybean Sci 3:013Google Scholar
- Thomas JW, Maser C, Rodiek JE (1979) Edges, vol 553. Wildlife habitats in managed forest: the Blue Mountains of Oregon and Washington. U.S. Department of Agriculture, Forest Service, Washington, D.C.Google Scholar
- Tiegu W, Xinliang Z, Huaisheng Z, Juan M, Shilin C (2012) Marginal effect of yield and correlation analysis with main agricultural traits in maize. Chin Agric Sci Bull 28(18):122–126Google Scholar
- Weisz R, Fleischer S, Smilowitz Z (1995) Site-specific integrated Pest Management for High Value Crops: sample units for map generation using the Colorado potato beetle (Coleoptera: Chrysomelidae) as a model system. J Econ Entomol 88(5):1069–1080. https://doi.org/10.1093/jee/88.5.1069 CrossRefPubMedGoogle Scholar
- Wu W, Li X (2002) Research on the marginal effect of wheat experimental plot. Acta Botan Boreali-Occiden Sin 23(12):2167–2171Google Scholar
- Yahner RH (1988) Changes in wildlife communities near edges. Conserv Biol 2(4):333–339. https://doi.org/10.1111/j.1523-1739.1988.tb00197.x CrossRefGoogle Scholar
- Zehnder G, Gurr GM, Kühne S, Wade MR, Wratten SD, Wyss E (2007) Arthropod pest management in organic crops. Annu Rev Entomol 52(1):57–80. https://doi.org/10.1146/annurev.ento.52.110405.091337 CrossRefPubMedGoogle Scholar
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