In intensively managed agroecosystems, the establishment of strips or other areas of flowering herbaceous plants, hereafter ‘flower strips’, and hedgerows are among the most commonly applied measures to achieve these goals . For example, the establishment of flower strips or hedgerows is supported by the Common Agricultural Policy in the European Union and by the Farm Bill in the United States . Typically established along field edges, flower strips and hedgerows offer resources for pollinators and natural enemies of crop pests such as shelter, oviposition sites, overwintering opportunities and food resources and can locally increase their abundance and diversity . It is less well understood whether enhanced species diversity translates to ex situ provisioning of pollination, pest control and increased yield. The ‘exporter’ hypothesis predicts enhanced delivery of ecosystem services through functional spillover from floral plantings . However, according the ‘concentrator’ hypothesis or the ‘Circe principle’ , resource-rich floral plantings temporarily compete with flowering crops and concentrate pollinators and natural enemies from the surrounding agriculture into the floral plantings, potentially resulting in reduced crop pollination and pest control services . This may explain why plantings fail to enhance crop pollination or pest control services, blueberry grow pot even if they successfully promote local pollinator or natural enemy abundance in restored habitats . The lack of clarity about effects of flower plantings on ecosystem service provisioning and crop yield scattered in numerous case studies is a barrier to farmer adoption of such measures .
A quantitative synthesis of such demonstrated broad evidence may assist farmers in making the decision to adopt these measures . Moreover, it is important to gain a general understanding of whether such effects are restricted to the area of the crop near to the adjacent planting or be detectable over larger distances . Such knowledge should be considered when designing schemes with optimal spatial arrangement of plantings across agricultural landscapes , and to facilitate cost-benefit assessments . To improve the effectiveness of flower strip and hedgerow plantings in promoting crop pollination, natural pest control, and potentially crop production, we need to better understand what determines their failure or success. We hypothesise that at least three factors influence the effectiveness of floral plantings in enhancing crop pollination and pest control services: plant diversity, time since establishment and landscape context. First, theory predicts that higher plant species richness, and associated trait diversity, promotes diverse pollinator and natural enemy communities due to positive selection and complementarity effects across space and time . However, the role of plant diversity driving effects of floral plantings on pollination and natural pest control services benefits to nearby crops is poorly understood. Second, time since the establishment of floral plantings is likely to play a key role for the local delivery of crop pollination and pest control services . This is of particular relevance for sown flower strips that may range from short-lived annual plantings to longer-lived perennial plantings.
Perennial plantings should offer better overwintering and nesting opportunities for pollinators and natural enemies and may foster local population growth over time . Third, the effectiveness of floral plantings could depend on the agricultural landscape context. Highly simplified landscapes likely have depleted source populations of pollinators and natural enemies. In complex landscapes, however, the ecological contrast introduced by floral plantings may not be great enough to result in strong effects . Strongest effects are therefore expected at intermediate landscape complexity . Although support for this hypothesis has been found with respect to biodiversity restoration , its validity for ecological intensification and the local delivery of crop pollination and pest control services has only just begun to be explored . Here we use data from 35 studies including 868 service site-year combinations across 529 sites in North American,European and New Zealand agroecosystems to quantitatively assess the effectiveness of two of the most commonly implemented ecological intensification measures, flower strips and hedgerows, in promoting crop pollination, pest control services and crop production. Moreover, we aim to better understand the key factors driving failure or success of these measures to suggest improvement of their design and implementation. Specifically, we address: the extent to which flower strips and hedgerows enhance pollination and pest control services in adjacent crops; how service provisioning changes with distance from floral plantings; the role of plant diversity and time since establishment of floral plantings in promoting pollination and pest control services; whether simplification of the surrounding landscape modifies the responses; and whether floral plantings enhance crop yield in adjacent fields.
Our synthesis reveals general positive effects of flower strips but not hedgerows on pest control services in adjacent crop fields. Effects on crop pollination, however, depended on flowering plant diversity and age since establishment, with more species-rich and older plantings being more effective. However, no consistent impacts of flower strips on crop yield could be detected, highlighting the need for further optimisations of plantings as measures for ecological intensification.To identify data sets suitable to address our research questions, we performed a search in the ISI Web of Science and SCOPUS . To minimise potential publication bias and to maximise the number of relevant data sets we also searched for unpublished data by contacting potential data holders through researcher networks. Data sets had to meet the following requirements to be included in the analysis: pollination and/or pest control services in crops were measured in both crop fields adjacent to floral plantings and control fields without planting; the replication at the field level was ≥ six fields per study . We contacted data holders fulfilling these requirements and requested primary data on plant species richness of plantings, time since establishment, landscape context and crop yield in addition to measured pollination and pest control services. Overall, we analysed data from 35 studies. We here define a study as a dataset collected by the same group of researchers for a particular crop species and ecosystem service in a particular region during one or several sampling years. We collected 18 pest control service and 17 pollination service studies, representing a total of 868 service-site-year combinations across 529 sites . In eight of these studies both crop pollination and pest control services were measured . Pollination services, pest control services and crop yield As different studies used different methods and measures to quantify pollination services, pest control services and crop yield, we standardised data prior to statistical analysis using z-scores . The use of z-scores has clear advantages compared with other transformations or standardisation approaches because average z-scores follow a normal distribution, and the variability present in the raw data is not constrained as in other indices that are bound between 0 and 1 . Pollination services were measured as seed set , fruit set , pollen deposition rate and, in one study, flower visitation rate . If available, differences in pollination service measures of openpollinated flowers and flowers from which pollinators were excluded were analysed. Measures of pest control services were quantified as pest parasitism , pest predation , square plastic pot population growth or crop damage by pests or pest densities . Whenever possible, the pest control index based on population growth proposed by Gardiner et al. was calculated and analysed . Note that standardised values of pest density and crop damage were multiplied by 1 because lower values of these measures reflect an increased pest control service . Too few studies assessed crop quality which was therefore not considered further. Yield was measured as crop mass or number of fruits produced per unit area. Due to a lack of studies measuring crop yield in fields with and without adjacent hedgerows, the analysis of crop yield focused on effects of flower strips. Crop yield measures were available from a total of 11 flower strip studies and 194 fields .Flower strips are here defined as strips or other areas of planted wild native and/or non-native flowering herbaceous plants. Hedgerows are defined as areas of linear shape planted with native and/or non-native at least partly flowering woody plants and typically also herbaceous flowering plants. For hedgerows, information about the exact time since establishment and number of plant species was not available for most studies. The analyses of these drivers therefore focus on flower strip effects on pollination and pest control services. Information on plant species richness was available in 12 out of 18 pest control studies and 10 out of 17 pollination studies. Whenever available, the species richness of flowering plants was used.
Otherwise, for some flower strip studies, the number of sown, potentially flowering plant species was used. Time since establishment of flower strips, that is the time span between seeding or planting and data sampling, was available for all studies ranging from 3 to 122 months. The proportional cover of arable crops was available and analysed as a proxy for landscape simplification in 11 pest control and 12 pollination studies. Proportional cover of arable crops was calculated in circular sectors of 1 km radius around focal crops, or 750 m or 500 m radius .We used a mixed effect-modelling approach to address our research questions. In all models, study was included as a random intercept to account for the hierarchical structure of the data with field measures nested within study. To assess whether flower strips and hedgerows enhanced pollination and pest control services in adjacent crops linear mixed-effect models with planting were separately fitted for flower strips and hedgerows for the response variables pollination service and pest control service. To test how the effects on service provisioning change with distance from plantings and with landscape simplification these explanatory variables and their interactions with the fixed effects described earlier were included in the models. Exploratory analyses showed that neither distance nor landscape simplification effects differed between flower strips and hedgerows; that is no significant interactive effects of planting type with any of the tested fixed effects. We therefore pooled flower strip and hedgerow data in the final models, excluding planting type and its two or three-way interactions as fixed effects. In addition to linear relationships we tested for an exponential decline of measured response variables from the border of the field by fitting log10 in the linear mixed-effect models described earlier. In this case, field nested within study was included as a random effect. To test the intermediate landscape complexity hypothesis, we tested for linear as well as hump-shaped relationships between landscape context, and its interaction with local floral plantings by fitting landscape variables as a quadratic fixed predictor in the models described earlier . To present the ranges covered by the agricultural landscape gradients, we did not standardise measures of landscape simplification within studies . To examine how pollination and pest control service provisioning relates to flower strip plant diversity and time since establishment plant species richness and log10 were included as fixed effects in models with study as a random effect. Using log predicted the data better than establishment time as linear predictor. Plant species richness and time since establishment of flower strips were not correlated . Only 10 studies measured services in several years since establishment , and we included only data from the last sampling year. To assess how the presence of plantings affected the agronomic yield of adjacent crops , we fitted a linear mixed-effect model with the same fixed and random structure as described for question 1, but with crop yield as the response variable. Statistical analyses for different models and response variables differed in sample sizes as not all studies measured crop yield in addition to pollination or pest control services . In all models we initially included planting area as a co-variate in an explorative analysis, but removed it in the final models, as it did not explain variation in any of the models and did not improve model fit . Effect sizes provided in the text and figures are model estimates of z-transformed response variables. For statistical inference of fixed effects we used log-likelihood ratio tests recommended for testing significant effects of a priori selected parameters relevant to the hypotheses . For all models, assumptions were checked according to the graphical validation procedures recommended by Zuur et al. . All statistical analyses were performed in R version 3.5.2 using the R-package lme4 .