Historical agroeconomic analysis of the relationship between commercial pollinator use and Vaccinium angustifolium (Ericales: Ericaceae) yield in Quebec, Canada (2015–2021)

Abstract

Lowbush blueberry (Vaccinium angustifolium Ait.) production has the highest export value among all fruit crops in Canada. As blueberry flowers require insect pollination, blueberry farms rely on commercial pollinators to ensure productivity of their fields. Our study aimed to assess the impact of commercial pollinator density and diversity on lowbush blueberry yield using historical data, from 2015 to 2021, across various production contexts. The analysis included 178 blueberry fields, representing approximately 3,000 ha and 11% of Québec’s total crop production area. Data on field size, berry yield, commercial pollinator density/diversity (honey bees; Apis mellifera L. [Hymenoptera: Apidae]), bumble bees; Bombus impatiens Cresson [Hymenoptera: Apidae], and alfalfa leafcutter bees; Megachile rotundata Fabr. [Hymenoptera: Megachilidae]), establishment year, and management system were collected for each field. Additionally, we examined data on honey bee hive strength, landscape structure, and meteorological factors influencing yield. Results showed significant yield increase at certain densities of honey bee hives and frames, bumble bee hives and alfalfa leafcutter bee gallons. The diversity of commercial pollinators used in the fields also increased crop yield. Other key factors influencing lowbush blueberry yield included the year, snow cover during winter, field age, and frost events during pollination. This study clarifies the relationship between commercial pollinators and lowbush blueberry productivity while accounting for several other agroeconomic variables, and demonstrates that increasing managed pollinator density and diversity lead to higher yields for lowbush blueberry growers.

Introduction

The lowbush blueberry (Vaccinium angustifolium Ait.), also known as the wild blueberry, is a plant of the Ericaceae family, native to northeastern North America (Hall et al. 1979, Bell et al. 2009). This crop produces approximately 95 million kilograms of fruit annually, with the majority of production originating from Canada. This industry plays a vital economic role in Canada, with exports exceeding $300 million, making it the country’s most significant fruit crop (AAC 2024b). Of the 68,000 ha cultivated in Canada, nearly 30,000 ha are in the Saguenay–Lac-Saint-Jean region of Quebec (MAPAQ 2022, IBO 2023, AAC 2024a, 2024b). However, Quebec lags behind other lowbush blueberry-producing regions such as the state of Maine in the United States and Canada’s Maritime provinces in terms of yield and market prices (Yarborough 2004, 2018, MAPAQ 2022, Paré et al. 2022). To increase profitability for producers, optimizing field productivity is crucial, and 1 potential avenue for improvement lies in enhancing pollination efficiency.

Pollination of lowbush blueberry flowers relies on insect activity for 91% of pollen transfer, which is critical for fertilization, optimal seed development, and a high-quality fruit harvest (Aalders and Hall 1961, Argall and Chiasson 1996, De Oliveira 1997, Stubbs and Drummond 2001, Yarborough 2012). Although nearly 100 species of wild pollinators have been identified in blueberry fields in the Saguenay–Lac-­Saint-Jean region, their contribution to yield remains limited (Argall et al. 1998, Stubbs et al. 2007, Moisan-De Serres et al. 2014b, Moisan-De Serres et al. 2015). To meet growing pollination needs of this crop, lowbush blueberry farms rely on commercial pollinators: the honey bee (Apis mellifera L.) (Hymenoptera: Apidae), the bumble bee (Bombus impatiens Cresson) (Hymenoptera: Apidae), and the alfalfa leafcutter bee (Megachile rotuhandata Fabr.) (Hymenoptera: Megachilidae) (Argall and Chiasson 1996, De Oliveira 1997, Javorek et al. 2002, Savard 2014).

The honey bee is the most commonly used pollinator for lowbush blueberries, due to its populous colonies, availability in large numbers, and ability to forage over long distances (Argall and Chiasson 1996, Drummond 2002). In the Saguenay–Lac-Saint-Jean region, producers rent nearly 30,000 honey bee hives annually, with the value of fruit production linked to honey bee pollination exceeding $200 million (AAC 2024b, Statistique Québec 2024). Quebec blueberry farms typically place 2.5 hives per hectare to pollinate their fields. However, several studies have reported increased yields with hive densities ranging from 4 to 12 hives per hectare (Aras et al. 1996, Drummond 2002, Eaton and Nams 2012, ATTTA 2017). As a result, recommended densities are variable and imprecise. Furthermore, the strength of honey bee colonies is rarely considered, and standards for hive strength differ across regions, making it difficult to compare results from different studies (Bennett and Byers 2023, Bernier et al. 2023). More research is needed to better understand the relationship between honey bee colony density and lowbush blueberry productivity.

Producers also use bumble bee hives to pollinate their fields. Due to their large size and ability to perform buzz pollination, bumble bees are highly effective at pollinating blueberry flowers, whose pollen is tightly held inside the anthers (Stubbs and Drummond 2001, Desjardins and De Oliveira 2006, Isaacs and Kirk 2010). Since 2017, the purchase of bumble bee hives has increased 6-fold due to challenges in acquiring honey bee hives (Roy 2023). While bumble bees are often used as a complement to honey bees, some blueberry farms in Quebec now rely on them as primary pollinators (SPBQ 2010, Roy 2023). The alfalfa leafcutter bee is another important commercial pollinator for blueberry fields. This species is particularly beneficial for small-scale producers as it forages within a 120-m radius of its nesting dome (MacKenzie et al. 1997, Stubbs and ­Drummond 1997, Javorek et al. 2002). Although the impact of bumble bees and leafcutter bees on blueberry yields has been demonstrated in multiple studies (Stubbs and Drummond 2001, Stubbs et al. 2002, Desjardins and De Oliveira 2006, Mallinger et al. 2021), the recommended densities for these 2 pollinators are based on a limited amount of research.

Some studies on other crops suggest that pollinator diversity may enhance pollination efficiency, either through complementarity or interactions between species (Greenleaf and Kremen 2006, Hoehn et al. 2008, Rogers et al. 2014, DeVetter et al. 2022). To our knowledge, only 1 explored this topic in lowbush blueberry, finding that a combination of honey bees and bumble bees led to higher fruit set (ATTTA 2017). Further research is needed to better understand the potential benefits of pollinator diversity in lowbush blueberry cultivation.

In addition to pollination efficiency, lowbush blueberry yield depends on several interconnected factors. Specifically, crop productivity can vary based on cultural practices, plant maturity, cultivar, soil fertility, pest or disease incidence, and the surrounding landscape composition (Hepler and Yarborough 1991, Yarborough 2012, Nicholson et al. 2017, Yarborough 2018). Weather conditions also play a significant role in determining crop yield from year to year. The northern climate of the Saguenay−Lac-Saint-Jean region presents particular challenges, with early autumn and late spring frost, along with periods of insufficient snow cover, exposing fruiting stems to winter frost. Additionally, unfavorable pollination conditions or lack of precipitations during the summer can limit the crop’s production potential (Hall and Forsyth 1967, Bouchard et al. 1987, Hicklenton et al. 2002, Yarborough 2002). Few studies consider the impact of environmental parameters, which can greatly influence the effectiveness of commercial pollination.

The objective of our study was to determine the effect of the density and diversity of commercial pollinator species on lowbush blueberry yield, while considering the influence of other factors such as field age, management practices, landscape structure, honey bee colony strength, and several meteorological variables. We hypothesized that the 3 commercial pollinators positively impact fruit production at a certain density, and that other agroeconomic factors also influence lowbush blueberry yield. To achieve this, we collected and analyzed historical agroeconomic data from 2015 to 2021, collected from multiple lowbush blueberry farms in the Saguenay−Lac-Saint-Jean region.

Materials and Methods

Lowbush Blueberry Fields

This study utilized historical agroeconomic data from 2015 to 2021, collected from 178 lowbush blueberry fields owned by 4 enterprises operating in the municipalities of Péribonka (48°83′56.000″N, 71°85′10.000″W), La Doré (48°75′01.000″N, 72°57′46.000″W), Saint-Eugène-d’Argentenay (49°02′66.000″N, 71°30′81.000″W), L’Ascension-de-Notre-Seigneur (48°71′56.000″N, 71°62′87.000″W), and Saint-David-de-Falardeau (48°63′97.000″N, 71°23′75.000″W), all within the Saguenay–Lac-Saint-Jean region of Quebec, Canada (Fig. 1). Each municipality represents a study site, in which there are multiple fields (Table 1). The total area of the fields was 2,981 hectares (1 hectare = 10,000 m²), representing 11% of the region’s production area (4.6% of the Canadian production). These enterprises annually rent or buy commercial pollinators to ensure the pollination of their lowbush blueberry fields, using one or a combination of the following species: A. mellifera (honey bee), B. impatiens (bumble bee), or Megachile rotundata (alfalfa leafcutter bee). The selected enterprises maintain detailed records that include, among other data, the fruit yield per field, the density of commercial pollinator units per field, and the strength of rented honey bee colonies (certified by a Quebec agronomist).

Fig. 1.

Location of the 5 municipalities (study sites) (site 1; Péribonka, site 2; La Doré, site 3; Saint-Eugène-D’Argentenay, site 4; L’Ascension-de-Notre-Seigneur, and site 5; Saint-David-de-Falardeau), of lowbush blueberry fields in the Saguenay–Lac-Saint-Jean region in Quebec, Canada. Map was created using Google Maps (©2024 Google).

Table 1.

Lowbush blueberry fields location, distribution among study sites and cultivated area (hectares)

Study site	Municipality	No. of fields	Total area of fields (ha)
1	Péribonka	25	544.6
2	La Doré	24	181.7
3	Saint-Eugène-D’Argentenay	42	138.4
4	L’Ascension-de-Notre-­Seigneur	41	986.8
5	Saint-David-de-Falardeau	46	1129.4

Agroeconomic Data from 2015 to 2021

Historical agroeconomic data were obtained directly from lowbush blueberry enterprises, in collaboration with the Club Conseil Bleuet (info@clubbleuet.com; Dolbeau-Mistassini, QC, Canada), an organization providing technical and agroenvironmental services to wild blueberry farms in Quebec. The agroeconomic variables used in this study are summarized in Table 2.

Table 2.

Summary of numerical agroeconomic variables collected for each lowbush blueberry field of the study (2015 to 2021) (n = 562)

Variable	Minimum/maximum	Mean  ±  SD
Area	0.3/98.2 ha	18.0 ± 12.9 ha
Yield	391.7/10733.7 kg/ha	2963.5 ± 1730.6 kg/ha
Age	2/58 years	19.8 ± 8.5 years
Honey bee density
Hives	0/10.3 hives/ha	2.8 ± 1.4 hives/ha
Frames  a	0/154.71 frames/ha	36.3 ± 26.8 frames/ha
Bumble bee density	0/3 hives/ha	0.3 ± 0.1 hive/ha
Alfalfa leafcutter bee density	0/11.7 gallons/ha	1.5 ± 1.1 gallons/ha
No of commercial pollinator species	0/3 species	1.7 ± 0.9 species
Foraging potential in a 5 km radius	50.3/74.9%	60.1 ± 7.5%

^aFor this variable, n = 395, since honey bee colony strength data were not available for all fields-years.

The following variables were collected for each field (n = 178 fields), from 2015 to 2021: field area, measured in hectares (ha); field management system, which included 3 types: (i) organic management, where the use of synthetic products (herbicides, insecticides, fungicides, and chemical fertilizers) is prohibited and the system is certified by a body accredited by the Reserved Designation and Added-Value Claims (Conseil des appellations réservées et des termes valorisants, CARTV); (ii) Boreal Wild Blueberries certification, where blueberries are sourced from the ecological zone of Quebec’s boreal forest, with restrictions on the use of synthetic products 12 mo before harvest, also certified by a CARTV-accredited body; and (iii) conventional management, where no specific restrictions apply; field age, calculated by subtracting the year the field was established (from 1963 to 2017) from the year of the data collection (2015 to 2021); crop rotation year, which can be the vegetative year or the first production year (the second production year data were excluded from the study since they represented only 2% of the fields); and field yield, measured in kilograms of blueberries per hectare (kg/ha), calculated by dividing fruit production of the field (kg) by the area of each field.

Commercial pollination data were also obtained for each field from 2015 to 2021. Commercial pollinator density was recorded for 3 species: (i) honey bee density, measured in hives per hectare (hive/ha) and categorized into 7 equally distributed groups: 0 to 1, 1 to 2, 2 to 3, 3 to 4, 4 to 5, 5 to 6, and ≥6 hives/ha; (ii) bumble bee density, measured in hives per hectare (hive/ha) and categorized into 4 equally distributed groups: 0, 0 to 0.5, 0.5 to 1, and ≥1 hive/ha, with 1 hive representing a group of 4 colonies (referred to as a “Quad” by Koppert [Berkel en Rodenrijs, The Netherlands] or a “4-Pak hive” by BioBest [Westerlo, Belgium]); and (iii) alfalfa leafcutter bee density, measured in gallons per hectare (gallon/ha) and categorized into 4 equally distributed groups: 0 to 1, 1 to 2, 2 to 3, and ≥3 gallons/ha, with bees distributed in dome-shaped shelters containing approximately 10 gallons of cocooned brood, equivalent to 100,000 cocoons (1 gallon ≈ 10,000 cocoons). Honey bee colony strength data was also obtained, determined by the number of frames covered with bees per hive (using the top-bottom method; Büchler et al., 2013). The evaluations of colony strength are certified by Nicolas Tremblay (agr.) and were performed on 25% of colonies randomly selected at each study site. A total of 4,698 hives were evaluated between 2015 and 2021. The average hive strength was calculated for each field-year and then multiplied by honey bee hive density to obtain a honey bee frame density (frame/ha). Frame density data were categorized into 5 equally distributed groups: 0 to 15, 15 to 30, 30 to 45, 45 to 60, and ≥60 frames/ha, and data for some years were missing for certain fields (n = 395). The diversity of commercial pollinators, that is the number of commercial pollinator species used in fields for pollination, was also calculated. The values range from none to 1, 2, or all 3 species among A. mellifera, B. impatiens, and M. rotundata.

Landscape Structure Analysis

To evaluate the impact of the agroenvironmental landscape surrounding lowbush blueberry fields on yield, we characterized the landscape structure within a 5 km radius of each site. This distance was chosen because it corresponds to the maximum foraging range of honey bees (Winston 1987), although their foraging radius rarely exceeds 3 km. Data were extracted from various sources, including forest databases from the Ministère de l’Énergie et des Ressources naturelles du Québec (MRNF 2024) and commercial crops data from La Financière Agricole du Québec (FADQ 2015–2021). All analyses were performed using a geographic information system (ArcGIS Pro 3.2, ESRI 2011).

Each plot within the 5 km radius was characterized either as melliferous/polliniferous (high foraging potential) or nonmelliferous/polliniferous (low foraging potential). Melliferous and polliniferous plants, that is plants that produce nectar and/or pollen harvested by bees, were determined according to ­Moisan-De Serres et al. (2014a), considering the blooming period of plants. Plots with high foraging potential are characterized by the presence of melliferous and polliniferous plants blooming during the lowbush blueberry pollination period, which occurs in June, while plots with low foraging potential are characterized by the absence of melliferous and polliniferous plants blooming in June. High foraging potential areas include mixed and deciduous forests, melliferous and pollinating crops (such as fruits), wetland clearings (presence of flowering Ericaceae), alder forests, fallow land with dandelions (Taraxacum sp.), as well as wastelands and grazing areas. Low foraging potential areas include coniferous forests, nonmelliferous or nonpollinating crops (such as potatoes, various cereals, etc), urban areas (roads, etc.), and bodies of water.

Based on these data, a map foraging potential was created for each site (5 sites) and year (6 years) (see Supplementary Fig. S1 for 2021 maps). Maps have been created for each site, not for each field, since fields at the same site are close to each other. From these maps, the percentage of foraging potential was calculated for each site and year, by measuring the proportion of the total map area occupied by high foraging potential zones.

Meteorological Data

To assess the impact of weather conditions on lowbush blueberry yield, meteorological data points were extracted from the Environment Canada website (Environment Canada 2015–2021) for stations near each of the 5 sites. The meteorological stations and their corresponding locations are: Normandin (48°50′30.000″N, 72°32′49.000” W; 26.9 km and 10.4 km of sites 1 and 2, respectively), Péribonka (48°46′00.000″N, 72°02′00.000″W; 15.4 km of site 3), Mistook (48°35′54.000″N, 71°42′57.000″W; 14.5 km of site 4), and Saint-Ambroise (48°34′00.000″N, 71°20′00.000″W; 10.8 km of site 5).

Meteorological data were collected for each year during 3 key periods that affect lowbush blueberry yield: the winter period (November to April), the blueberry pollination period (∼late May to mid-June), and the blueberry fruit growing period (∼mid-June to mid-August) (Hall and Forsyth 1967, Hall et al. 1982, Bouchard et al. 1987, Hicklenton et al. 2002). The pollination and harvest periods for each year were determined from seasonal reports of Agri-Réseau (MAPAQ 2015, 2016, 2017, 2018, 2019, 2020, 2021). The specific weather variables used were as follows: during the winter period; daily minimum and maximum temperatures (°C), snow cover (cm) and the occurrence of extreme cold (number of days with minimum temperature ≤−25 °C); during the pollination period; daily minimum and maximum temperatures (°C) and the occurrence of flower frost (number of days with minimum temperature ≤ −1 °C); and during the fruit growth period; daily precipitation (mm).

These variables have been shown to influence lowbush blueberry productivity (Lockhart 1961, Hall and Forsyth 1967, Bouchard et al. 1987, Hicklenton et al. 2002, Yarborough 2002). The extreme cold temperature threshold during winter (−25 °C) was selected because it represents the temperature at which rhizomes and plant stems may incur damage (Bouchard et al. 1987, SPBQ 2008), and the flower freezing temperature threshold (−1 °C) was chosen because it represents the temperature at which flowers can experience observable damage (MAPAQ 2014). For all meteorological variables across the 3 periods, averages were calculated for each site and year.

Statistical Analyses

Statistical analyses were performed using R software (v4.1.2, R Core Team 2021), and the results were interpreted with a significance threshold of 0.05.

Several mixed linear models (nlme::lme) (v3.1-155, Pinheiro et al. 2022) were used to assess the effect of the various variables on lowbush blueberry yield, namely field age, management system, foraging potential in a 5 km radius, the density and diversity of the 3 commercial pollinators, and all the meteorological variables. The nested random effects of the field, site, and blueberry farm, as well as the fixed effect of the year, were included in all models. The model analyzing the effect of commercial pollinators and their diversity on yield also included the fixed effects of field age, management system, foraging potential in a 5 km radius, and all meteorological variables. The effect of each fixed effect was determined using analysis of variance tables with the emmeans::joint_tests (v1.7.2, Lenth 2022) function. When a significant difference was present, pairwise comparisons were made using Tukey-adjusted tests to identify where the differences occurred (functions emmeans::emmeans and emmeans::pairs). The results were graphically represented using the ggplot2 package (v3.5.2, Wickham 2016).

The R-squared values were estimated using the r2glmm::r2beta function (v0.1.2, Jaeger 2007), with the Kenward–Roger method. A model comparison was also performed through sequential regression analysis using the MASS::stepAIC function (v4.1.2, Venables and Ripley 2002), employing forward, backward, and both directions. The model comparison was conducted on the data where the honey bee density was expressed in hives per hectare (n = 562) and on the data where the honey bee density was expressed in frames per hectare (n = 395).

The normality of the scaled residuals across the models was validated using a histogram, a Shapiro–Wilk test (stats::shapiro.test) (v4.1.2, R Core Team 2021), and the values of skewness and kurtosis (functions moments::skewness and moments::kurtosis) (v0.14.1, Komsta and Novomestky 2022). Yield data were transformed to their square root since model residuals were not normally distributed. Homoscedasticity was validated with a residuals versus predicted values plot. In the presence of heteroscedasticity, a weights argument was added to model the heterogeneous variances of the problematic factor. Data on honey bee, bumble bee, and alfalfa leafcutter bee density were transformed into categorical variables to meet the homoscedasticity conditions. The chosen categories grouped an equivalent number of data points (categories are described in Section 2.2.1). Multicollinearity was assessed using the olsrr::ols_coll_diag (v.0.6.0, Hebbali 2024) and car::vif (v4.1.2, Fox and Weisberg 2019) functions. When all meteorological variables were included in the models, some variables (minimum temperatures during pollination, and minimum temperatures and occurrence of extreme frosts during winter) were removed to avoid multicollinearity issues. Since some variables (foraging potential and meteorological variables) were not available for each field but only for each site, the degrees of freedom for these variables were adjusted by including the interaction of these variables with the site.

Results

Descriptive Statistics

The range, mean, and standard deviation of the numerical agroeconomic data collected for this study are summarized in Table 2. A summary of the meteorological data included in this study can be found in the supplementary table (Supplementary Table S1). Regarding field management systems, 43% of fields were under organic management, 38% were certified as Boreal Wild Blueberries, and 19% were managed conventionally.

Effect of Commercial Pollinator Density on Yield

Honey Bees

The fruit yield of blueberry fields is significantly influenced by the density of honey bee hives (F-ratio = 35.664, df = 6; 332, P < 0.0001) and frames (F-ratio = 12.521, df = 4; 188, P < 0.001). Models including either hive density or frame density explain 46.6% and 43.8% of the variation in fruit yield, respectively.

When honey bee density is measured in hives per hectare (Fig. 2A), the lowest yields are observed in the 2 lowest hive density groups (0 to 1 and 1 to 2 hives/ha), with yields of 2,526 ± 825 kg/ha (mean ± SE) and 2,232 ± 765 kg/ha, respectively. Yield increases significantly at a density of 2 to 3 hives/ha (t-ratio = −4.04, df = 6; 332, P = 0.0013). Above 2 hives/ha, the density groups are equivalent, with yields of 3,762 ± 978 kg/ha (2 to 3 hives/ha), 4,292 ± 1,121 kg/ha (3 to 4 hives/ha), 3,209 ± 951 kg/ha (4 to 5 hives/ha), 3,717 ± 1,020 kg/ha (5 to 6 hives/ha), and 4,195 ± 1,118 kg/ha (≥ 6 hives/ha).

Fig. 2.

Mean (±SE) yield (kg blueberries/ha) of lowbush blueberry fields in relation to A) honey bee hive density (hive/ha, n = 562) and B) frame density (frame/ha, n = 395) used during pollination period from 2015 to 2021, for the 5 study sites located in the Saguenay–Lac–Saint–Jean region. Yield values are adjusted for the density of other commercial pollinators, year, crop management system, field age, weather conditions, field, site, and blueberry farm and have been back-transformed to their original scale for visualization purposes. Statistically different groups (P < 0.05) are indicated by different letters above groups.

When honey bee density is measured in frames per hectare (Fig. 2B), the lowest yield (2,442 ± 783 kg/ha) is observed at the lowest frame density group (0 to 15 frames/ha). Yield increases progressively with increasing frame densities of 30 to 45 frames/ha (t-ratio = −4.142, df = 4; 188, P = 0.0005), 45 to 60 frames/ha (t-ratio = −5.904, df = 4; 188, P < 0.0001), and ≥ 60 frames/ha (t-ratio = −6.674, df = 4; 188, P < 0.0001), which achieve respective yields of 3,641 ± 952 kg/ha, 4,272 ± 1051 kg/ha, and 4,709 ± 1,099 kg/ha. The highest yield is obtained at the highest frame density (≥60 frames/ha), which is significantly higher than the 0 to 15 frames/ha group (t-ratio = −6.672, df = 4; 188, P < 0.0001), the 15 to 30 frames/ha group (3,083 ± 759 kg/ha | t-ratio = −4.755, df = 4; 188, P < 0.0001), and the 30 to 45 frames/ha group (t-ratio = −3.475, df = 4; 188, P = 0.0056).

Bumble Bees

Fruit yield is significantly influenced by bumble bee density (F-ratio = 3.513, df = 3; 332, P = 0.016) (Fig. 3A). The group without bumble bees has the lowest yield (2,809 ± 880 kg/ha). Yield significantly increases at a density of over 1 hive/ha (t-ratio = −3.165, df = 3; 332, P = 0.0091), which has the highest yield at 3,851 ± 1,083 kg/ha. Intermediate densities of 0 to 0.5 and 0.5 to 1 hive/ha produce yields equivalent to lower or higher densities (respectively 3,010 ± 926 and 3,286 ± 975 kg/ha).

Fig. 3.

Mean (±SE) yield (kg blueberries/ha) of lowbush blueberry fields (n = 562) in relation to A) bumble bee density (hive/ha) and B) alfalfa leafcutter bee density (gallons/ha) used during pollination period from 2015 to 2021, for the 5 study sites located in the Saguenay–Lac–Saint–Jean region. Yield values are adjusted for the density of other commercial pollinators, year, crop management system, field age, weather conditions, field, site, and blueberry farm and have been back-transformed to their original scale for visualization purposes. Statistically different groups (P < 0.05) are indicated by different letters above groups.

Alfalfa Leafcutter Bee

Fruit yield is significantly influenced by alfalfa leafcutter bee density (F-ratio = 7.262, df = 3; 332, P = 0.0001) (Fig. 3A). Yield significantly increases at densities over 3 gallons/ha, which has a yield of 3,972 ± 1,096 kg/ha (t-ratio = −4.582, df = 3; 332, P < 0.0001). This yield is significantly higher than for the densities of 0 to 1 gallons/ha (t-ratio = −2.799, df = 3; 332, P = 0.0276) and 1 to 2 gallons/ha (t-ratio = −3.165, df = 3; 332, P = 0.0091), which yield 2,997 ± 906 kg/ha and 2,738 ± 897 kg/ha, respectively. The 2 to 3 gallons/ha density has an intermediate yield of 3,265 ± 971 kg/ha, which is equivalent to the 3 other densities.

Effect of Commercial Pollinator Diversity on Yield

Fruit yield is significantly influenced by the number of commercial pollinator species used in the fields (F-ratio = 8.910, df = 3; 329, P < 0.0001). Fields without commercial pollinators have the lowest yield (1,603 ± 821 kg/ha), while fields with all 3 species of commercial pollinators have the highest yield (3,455 ± 790 kg/ha). This yield is higher when compared to fields without commercial pollinators (t-ratio = −3.995, df = 3; 329, P = 0.0005), with 1 pollinator specie (t-ratio = −3.490, df = 3; 329, P = 0.0031), and with 2 pollinator species (t-ratio = −2.793, df = 3; 329, P = 0.0281). Fields with 1 or 2 pollinator species are equivalent (t-ratio = −1.529, df = 3; 329, P = 0.4213), yielding 2,607 ± 964 kg/ha and 2,906 ± 1,031 kg/ha, respectively. These 2 groups achieve a higher yield than fields with no pollinator (t-ratio = −2.682 and −3.112, P = 0.0383 and 0.0108, respectively, df = 3; 329). No interaction between the densities of the different commercial pollinators has a significant effect on yield.

Influence of Other Agroeconomic Variables on Yield

The other variables having a significant effect on yield are the year (F-ratio = 20.671, df = 6; 332, P < 0.0001), snow cover during the winter (F-ratio = 17.076, df = 1; 332, P = 0.0004), occurrences of flower frost during the pollination period (F-ratio = 8.519, df = 1; 332, P = 0.0075), and field age (F-ratio = 12.226, df = 1; 332, P = 0.0005). Snow cover and field age have a positive effect on yield, while flower frost occurrences have a negative impact on yield.

Comparison of Predictive Models of Yield

The models were constructed according to the different categories of measured variables, as described in Table 3.

Table 3.

Comparison of predictive models of yield based on AIC values, an estimator of the quality of a model

Data set	Model name	Explanatory variables (fixed effects)	df	AIC	ΔAIC	r  2
Data set with hive density (  n  = 562)	stepAIC	All pollinator densities + age + management system + snow cover (winter) + daily precipitations (growth)	333	4,434.31	0	0.488
	all	All variables	332	4,454.84	20.53	0.476
	pollinators	Hive-density + bumble bee density + leafcutter bee density	337	4,470.47	36.16	0.469
	weather.winter	Snow cover + Min. temperatures (winter period)	347	4,532.33	98.02	0.365
	weather.all	Min. temperatures (pollination) + snow cover (winter) + daily precipitations (growth)	346	4,534.37	100.06	0.329
	field.caract	Age + management system	347	4,545.78	111.47	0.414
	weather.pollination	Frost occurrences + Min. temperatures (pollination)	347	4,562.83	128.52	0.316
	weather.growth	Daily precipitations (growth)	348	4,565.13	130.82	0.299
Data set with frames density (  n  = 395)	stepAIC.2	Frames density + snow cover (winter) + alfalfa leafcutter bee density + age + management system	193	3,185.67	0	0.351
	all.2	All variables	188	3,191.64	5.97	0.360
	pollinators.2	Honey bee frames density + bumble bee ­density + ­leafcutter bee density	193	3,199.58	13.91	0.306
	field.caract.2	Age + management system	193	3,236.25	50.58	0.245
	weather.all.2	Min. temperatures (pollination) + snow cover (winter) + daily precipitations (growth)	193	3,246.44	60.77	0.213
	weather.winter.2	Snow cover + min. temperatures (winter period)	193	3,250.43	64.76	0.216
	weather.growth.2	Daily precipitations (growth)	193	3,253.56	67.89	0.189
	weather.pollination.2	Frost occurrences + min. temperatures (pollination)	193	3,256.64	70.97	0.183

Lower AIC and Δ AIC values indicate a better fitting model for a given data set. All models include nested random effects of farm/site/field and fixed effect of year, and other fixed effects differ. df is the number of degrees of freedom of the model. AIC is the Akaike information criterion, and Δ AIC is the difference of AIC with the best model. r  ² is the conditional coefficient of determination and indicates the amount of variation explained by the model, including the influence of random effects.

When honey bee density is expressed in hives per hectare, the best model includes all explanatory variables except for the melliferous potential and the minimum temperatures during the pollination period, and explains 49% of the variation in yield. The best predictive variables are, in decreasing order, the density of pollinators, winter weather conditions, combined weather conditions during all 3 periods, field characteristics (age and management system), weather conditions during the pollination period, and finally, weather conditions during the blueberry growth period.

When honey bee density is expressed in frames per hectare, the best model includes the density of honey bee and alfalfa leafcutter bee, snow cover during winter, as well as field age and management system, and explains 35% of the variation in yield. The best predictive variables are, in decreasing order, the density of pollinators, field characteristics, combined weather conditions during all 3 periods, weather conditions during winter, weather conditions during the blueberry growth period, and finally, weather conditions during the pollination period.

Whether honey bee density is expressed either in hives or in frames per hectare, the explanatory variable that most effectively minimizes the Akaike information criterion (AIC) is honey bee density, followed by snow cover during winter.

Discussion

Using historical data from lowbush blueberry farms in the Saguenay–Lac-Saint-Jean region, our study highlights the importance of the 3 commercial pollinators on the productivity of crops. The honey bee, bumble bee, and alfalfa leafcutter bee all contribute to increase blueberry yields when introduced at certain densities. The highest yields were observed when all 3 species are used together in the same field.

To the best of our knowledge, this study is the first to assess the impact of the density of all 3 commercial pollinators on lowbush blueberry yield over an extended period, while also accounting for agroeconomic and meteorological factors. Additionally, our findings suggest that honey bee density should be expressed in frames of bees per hectare rather than hives per hectare, as hive strength varies significantly (from 4.4 to 19.1 frames per hive). Quebec lowbush blueberry growers would benefit from assessing the strength of their honey bee colonies to ensure pollination efficiency.

Commercial Pollinator Density and Crop Yield

Among all models predicting crop yield, the one including the density of commercial pollinators explains the largest proportion of variation in fruit yield, accounting for 39% of yield variation. Pollinator density has a greater impact on yield than all meteorological and agroeconomic factors (field age, management system, and foraging potential in a 5 km radius). This highlights the crucial role of commercial pollinators in lowbush blueberry production and confirms the need for further research on this subject. Of the 3 pollinators, the honey bee accounts for the greatest variation in yield, followed by the alfalfa leafcutter bee and the bumble bee.

Honey Bee Density

Honey bee density is the explanatory variable with the greatest impact on yield, as it most effectively minimizes the AIC of our models and demonstrates the most significant effect on productivity. In our study, we observe an increase in lowbush blueberry productivity starting at a density of 2 to 3 hives per hectare. These results are consistent with several studies indicating that at least 2.5 hives per hectare are necessary for adequate pollination of lowbush blueberry fields (De Oliveira 1995, Savoie and De Oliveira 1995, Drummond 2002). Specifically, De Oliveira (1995) tested a range of honey bee densities on lowbush blueberry yield over multiple years and found that field productivity significantly improved when honey bee densities reached 2.5 hives per hectare. Industry guidelines also support this, with recommendations stating that an average density of 2.5 hives per hectare is required to qualify for crop insurance coverage from the Financière Agricole du Québec (FADQ 2024). When expressed in frames per hectare, the results show the same pattern, with a significant effect on yield observed from 30 to 45 frames per hectare, which corresponds to approximately 2.3 to 3.5 hives per hectare (∼13 frames per hive).

Our results show that beyond 2 to 3 hives per hectare, increasing hive density no longer affects yield, suggesting that yield is no longer limited by hive density. However, when honey bee density is measured in terms of frames per hectare, field yield continues to increase up to a density of 45 to 60 frames per hectare, equivalent to approximately 3.5 to 4.6 hives per hectare. Many other studies have reported similar findings. For example, Eaton and Nams (2012) collected data on bee density and yield from 1991 to 2010 and showed a linear increase in yield up to a density of about 4 hives per hectare. Similarly, a study in the Canadian province of New Brunswick found that the number of berries per stem plateaued at a density of 4.9 hives per hectare (ATTTA 2017). However, some studies recommend densities as high as 12 hives per hectare for optimal yield (Savoie and De Oliveira 1995, Drummond 2002, ATTTA 2017). In our study, we cannot predict the effect of bee density beyond 60 frames per hectare (i.e. 4.6 hives per hectare) due to a lack of historical data at higher densities. Additional yield data for densities exceeding 60 frames per hectare would be valuable to pinpoint the threshold at which field productivity no longer increases with higher honey bee density. According to our findings, yield increases progressively with honey bee frame density, suggesting that yield is at least partially limited by honey bee frame density. However, the optimal density may vary depending on factors not assessed in this study, such as the presence of wild pollinators in the environment, the floral density of blueberry fields, the spatial distribution of hives, or the self-fertility of clones (Drummond 2002, Eaton et al. 2004, Bell et al. 2010, Fulton et al. 2015, ATTTA 2019, Rollin and Garibaldi 2019). Nevertheless, our results suggest that fruit yield could increase by almost 20% by installing at least 45 frames per hectare (∼3.5 hives/ha). Increasing bee density could however have detrimental effects on colony health (Pettis et al. 2013, Dufour et al. 2020, Tuerlings et al. 2022), and further studies are needed in the context of wild blueberry production to identify optimal stocking rates that enhance field productivity without compromising colony health.

It is interesting to note that in our study, the impact of honey bee density on crop yield varies depending on whether it is measured in hives or frames per hectare. Honey bee hives used for pollination come from multiple beekeepers and are therefore not all managed in the same way. Although the quality and size of colonies sent for pollination are generally expected to meet certain standards (Bennett and Byers 2023, Bernier et al. 2023), these standards are not always consistently met. We observed a high variability in colony strength, ranging from 4 to 19 frames of bees per hive. As a result, the number of bees in the fields is not necessarily proportional to the number of hives. A study on highbush blueberry also found that hive density was not correlated with the visitation rate of blueberry flowers by bees (Mallinger et al. 2021). However, when colony size was included in their analyses, bee visitation rate increased with hive density. Moreover, yield and fruit set were not predicted by hive density but rather by bee visitation rate (Mallinger et al. 2021). Another study on highbush blueberry suggests that more effective pollination could be achieved by increasing only colony strength, without increasing hive density (Grant et al. 2021).

The plateau observed when honey bee density is measured in hives per hectare may also be caused by their generalist foraging behavior, which could make them less efficient at pollinating lowbush blueberry flowers compared to bumble bees or alfalfa leafcutter bees (Javorek et al. 2002, Isaacs and Kirk 2010, Drummond 2012, Dufour et al. 2020). Consequently, when other competitive floral resources are abundant in the surrounding landscape, bee density is not necessarily proportional to the visitation rate of crop flowers (Pettis et al. 2013, Gaines-Day and Gratton 2016, Rollin and Garibaldi 2019). The landscape surrounding blueberry fields generally provides several attractive resources for honey bees during the lowbush blueberry pollination period (Dufour et al. 2020). In our study, the foraging potential within a 5 km radius around the blueberry fields was high, ranging from 50% to 75%. Thus, the presence of other blooming floral resources, such as dandelion, raspberry, hawthorn, and various other tree and shrub species (Agri-Réseau 2020), could reduce the attractiveness of blueberry flowers to honey bees.

Bumble Bee Density

B. impatiens is an effective pollinator of lowbush blueberry flowers due to its large size, buzz pollination, fast foraging speed, and strong fidelity to the blueberry crop (Whidden 1996, Stubbs and Drummond 2001, Javorek et al. 2002, Stubbs et al. 2002, Isaacs and Kirk 2010). Its use is common among lowbush blueberry growers, who often employ it in combination with honey bees or, more rarely, as the primary pollinator (SPBQ 2010, Roy 2023). Our results show that the bumble bee efficiently pollinates blueberry fields and that growers could achieve significantly higher yields by increasing bumble bee density to more than 1 hive per hectare.

Several studies have reported similar findings. For example, Desjardins and De Oliveira (2006) showed that bumble bee density in lowbush blueberry fields was significantly correlated with fruit set and seed size. They recommend a density of 688 bumble bees per hectare, equivalent to approximately 4 hives per hectare (Stubbs and Drummond 2001, Stubbs et al. 2002). Mallinger et al. (2021) also observed that fruit set and field yield increased up to a density of 1 hive per hectare. Other sources suggest that densities of up to 2.5 hives per hectare may be beneficial when no other commercial pollinators are present (Stubbs and Drummond 2001, Drummond 2012). In contrast, our study shows that growers in the region typically use a low bumble bee density, averaging 0.3 ± 0.1 hives per hectare. As a result, we do not have sufficient data at densities greater than 1 hive per hectare to accurately separate them into groups and determine when yield significantly increases. This limits our ability to predict the effect of bumble bee density beyond 1 hive per hectare.

The use of bumble bees as pollinators of the lowbush blueberry has grown in recent years, enabling us to refine our understanding of the relationship between bumble bee density and fruit yield. In the Saguenay–Lac-Saint-Jean region, the number bumble bee hives purchased increased from fewer than 2,000 hives in 2017 to nearly 11,000 in 2023, partly in response to challenges with honey bee hive availability (MAPAQ 2022, Roy 2023). Indeed, fluctuations in winter mortality rates and rising hive prices have led growers to diversify their pollinator species (AAC 2024a, Statistique Québec 2024).

It is important to note that our results do not account for the placement of colonies in the lowbush blueberry fields or the strength of the colonies, both of which can significantly influence their pollination efficiency. The foraging radius of bumble bees rarely exceeds 400 m from their colony, unlike honey bees, which can cover distances of up to 5 km. Additionally, their efficiency decreases beyond 150 m from their colony (Desjardins and De Oliveira 2006). Therefore, it is recommended that bumble bee hives be spaced apart to ensure uniform pollination (Desjardins and De Oliveira 2006). Colony strength, which varies between colonies and suppliers could also influence the impact of bumble bees on fruit yield (Stubbs et al. 2002, Biobest 2024, Robert et al. 2025 [manuscript in preparation]).

Alfalfa Leafcutter Bee Density

The density of M. rotundata is the third most important explanatory variable in our predictive models of yield, after honey bee density and winter snow cover. Its significant impact on yield is observable at densities greater than 3 gallons per hectare. The effectiveness of M. rotundata in pollinating lowbush blueberry has been demonstrated in several studies (Argall et al. 1996, MacKenzie et al. 1997, Stubbs and Drummond 1997, Javorek et al. 2002). Some studies found that adding alfalfa leafcutter bees to lowbush blueberry fields increased fruit set by 30% compared to fields with only honey bee hives (­MacKenzie et al. 1997, Stubbs and Drummond 1997). These results were attributed to the high pollination efficiency of the species, its attraction to blueberry crops, and its short foraging distance (Argall et al. 1996, MacKenzie et al. 1997, Javorek et al. 2002). Alfalfa leafcutter bees typically forage within a distance of 120 m from their nest, making it a reliable pollinator for blueberries and particularly beneficial for small-scale producers (MacKenzie et al. 1997, Savard 2014). In general, the literature recommends a density between 5 and 7.5 gallons per hectare for optimal pollination (MacKenzie et al. 1997, Stubbs and Drummond 1997, Savard 2014), but further research is needed to refine this relationship. Our results show that the highest yield occurs at densities above 3 gallons per hectare. However, due to insufficient data at higher densities, these groups were merged preventing us from predicting yield beyond this density. More data at higher densities are needed to make precise recommendations. Overall, our results confirm that M. rotundata is an effective pollinator of lowbush blueberry and contributes to improve field productivity. Growers would benefit from increasing the density of alfalfa leafcutter bees in their fields, as they currently use an average of 1.5 ± 1.1 gallons per hectare, which is below the recommended range.

Pollinator Diversity on Crop Yield

Lowbush blueberry productivity is influenced not only by the density of commercial pollinators, but also by their diversity. A greater pollinator diversity, defined here as the number of commercial pollinator species placed in the fields (ranging from 0 to 3), results in higher yields. In our study, a combination of the 3 pollinator species achieves the highest productivity. Pollinator diversity has been shown to positively affect the productivity of crops dependent on animals for pollination (Hoehn et al. 2008, Blüthgen and Klein 2011, Carvalheiro et al. 2011, Rogers et al. 2014). This phenomenon has been demonstrated in several crops including pumpkin, sunflower, apple, coffee, and highbush blueberry (Klein et al. 2003, Greenleaf and Kremen 2006, Hoehn et al. 2008, Rogers et al. 2014, Blitzer et al. 2016). Moreover, some studies suggest that pollinator diversity is a better predictor of pollination efficiency than pollinator density (Hoehn et al. 2008, Rogers et al. 2014).

The complementary nature of the 3 commercial pollinators could explain our results. First, the pollination activity of the 3 species is complementary temporally and environmentally, as they do not all forage at the same times of day, and have different tolerances to climatic and environmental conditions (McCall and Primack 1992, Hoehn et al. 2008, Fründ et al. 2013). Bumble bees tolerate colder temperatures (≥9 °C), but may take refuge in their colony when temperature rises during midday (∼27 °C) (Desjardins 2003, Fründ et al. 2013, ­Drummond 2016, Kenna et al. 2021). They are also highly tolerant to wind and rain (Stubbs et al. 2002, Desjardins 2003, Drummond 2016). In contrast, honey bees and alfalfa leafcutter bees need warmer temperatures to forage—around 14 °C for honey bees and 17 °C for alfalfa leaf cutter bees—and typically stay in their colonies during inclement weather (Szabo 1980, McCall and Primack 1992, Argall et al. 1996, ­Drummond 2016, Rogers et al. 2023). However, they are more tolerant to higher temperatures than bumble bees, being able to forage up to around 42 °C (Atmowidjojo et al. 1997, Barthell et al. 2002, Drummond 2016, Karbassioon et al. 2023). This complementarity is significant given the variability of temperature and weather during the lowbush blueberry flowering period. In our study, temperatures during the blueberry pollination period ranged from −5 to 36 °C. A variety of pollinators thus ensures temporal stability in crop pollination (Hoehn et al. 2008, Fründ et al. 2013, Rogers et al. 2014, Vasiliev and Greenwood 2021, Senapathi et al. 2021), which is becoming increasingly important in a context of climate change (Drummond 2016, Katumo et al. 2022).

At the field level, the 3 commercial pollinators are complementary because of their varying preferences for flower morphology, which differs between lowbush blueberry clones (Sampson et al. 2004, Courcelles et al. 2013, Cortés-Rivas et al. 2023). Honey bees generally prefer flowers with wide floral tubes and a large quantity of nectar (Courcelles et al. 2013, Bozek 2021). The long proboscis of bumble bees, on the other hand, allows them to forage from flowers of varying sizes (Goulson 2010, Cromie et al. 2024). Nectar foraging bumble bees show a preference for flowers with more nectar and those where floral density is higher, while pollen foraging bumble bees prefer longer and wider flowers (Courcelles et al. 2013, Drummond 2016, Cromie et al. 2024). These morphological preferences allow for more flowers to be pollinated and ensure more even pollination across fields (Courcelles et al. 2013, Sampson et al. 2013, Tucker et al. 2019, Katumo et al. 2022).

The interactions between pollinators could also enhance pollination efficiency and, therefore, crop productivity (Stout and Goulson 2001, Greenleaf and Kremen 2006, Blüthgen and Klein 2011, Carvalheiro et al. 2011). Specifically, the vibrational pollination by bumble bees may enhance the effectiveness of honey bees and Megachile species. Bumble bees shake the flowers releasing large amounts of pollen that become more accessible to other visiting bees (Blüthgen and Klein 2011, Drummond 2016). Additionally, pollinators can alter the behavior and movement of other species through chemical signals (Williams 1998, Stout and Goulson 2001). By collecting nectar or pollen from a flower, honey bees and bumble bees leave behind a chemical scent, signaling to other pollinators that the flower has already been visited. These signals improve foraging efficiency by preventing pollinators from visiting resource-depleted flowers (Williams 1998).

Thus, a diversity of pollinators, whether due to their complementarity or their interactions, raises the proportion of flowers pollinated and the quality of pollination. This is especially crucial with the decline of wild pollinators. More research is needed to better understand this topic, as most studies on pollinator diversity have focused on crops other than lowbush blueberry and do not exclusively consider commercial pollinators. It would also be interesting to test several combinations of commercial pollinators at various densities to identify most effective pollinator strategies for maximizing lowbush blueberry yield.

Influence of Weather and Other Variables

Several meteorological variables are known to reduce the productivity potential of lowbush blueberry crops. These include insufficient snow cover and extreme cold during the winter, late spring and early autumn frost, unfavorable pollination conditions during the flowering period of blueberries, or drought periods during fruit development (Lockhart 1961, Hall and Forsyth 1967, Bouchard et al. 1987, Hicklenton et al. 2002, Yarborough 2002). Our study confirms that insufficient snow cover and occurrence of frost during the flowering period have a significant negative impact on crop yield. During winter, temperatures below −25 °C can damage the dormant rhizomes and stems of lowbush blueberries. Therefore, adequate snow cover is critical, as it insulates the plants and protects them from the cold (Bouchard et al. 1987). In our study, snow cover is the most influential meteorological variable, ranking as the second most significant factor affecting yield, after honey bee density. Late spring frost is also very damaging to blueberry plants, as it prevents proper development of floral buds and flowers, which are the most frost-sensitive parts of the plant (Hicklenton et al. 2002, Yarborough 2002). Late spring frost is one of the main causes of low yields in some years (MAPAQ 2018, 2021). These results emphasize the importance of developing effective frost protection methods, such as the installation of windbreaks or irrigation (Argall and Chiasson 1996, Yarborough 2002). They also highlight the need to optimize pollination services to maximize crop production, especially under challenging weather conditions.

Field age also has a significant positive impact on yield. Although this relationship is not extensively documented for lowbush blueberry, it could be due to increased maturity of the plants or a higher plant density in the fields over time (Smagula and Yarborough 1990, Jamieson 2008, Douglass 2023). This result may also be caused by improvements in farming practices over time, such as more effective disease and pest control, or better management of fertilizers and other agricultural inputs (Yarborough 2004, Marty et al. 2019).

Among all the explanatory variables, the year itself accounts for the greatest variation in yield in all our models. This indicates that field productivity can vary greatly from 1 yr to the next, influenced by several other factors not assessed in this study. For example, yield is influenced by many other meteorological variables (early frost in the fall, extreme cold in winter, wind and sunlight during the pollination period, etc), farming practices, agricultural inputs used, the occurrence of diseases and pests, the abundance of wild pollinators in the environment, and many others. Including the influence of the year, field age, snow cover and spring frost occurrences in studies on lowbush blueberry pollination is nevertheless essential, as it can significantly affect the observable effects of pollination.

This study highlights the crucial role of the 3 commercial pollinators in enhancing the productivity of lowbush blueberry crops, while also acknowledging the significant influence of several agroeconomic and meteorological variables. Our research also reveals the need for colony strength requirements for honey bee colonies used for pollination, and emphasizes the importance of assessing colony strength to ensure an effective pollination.

Our results suggest that increasing the density and diversity of commercial pollinators, alongside better management of honey bee colony strength, could lead to better yields. This indicates that the productivity of lowbush blueberries in eastern Canada is currently constrained, in part, by insufficient pollination services. By clarifying the relationship between commercial pollination and lowbush blueberry production, our study provides valuable insights for producers to optimize their pollination strategies, ultimately enhancing the profitability and sustainability of the lowbush blueberry industry.

Author Contributions

Mireille Levesque (Conceptualization [supporting], Data curation [lead], Formal analysis [lead], Investigation [lead], Methodology [lead], Project administration [equal], Software [lead], Validation [equal], Visualization [lead], Writing—original draft [lead], Writing—review & editing [lead]), Frédéric McCune (Conceptualization [supporting], Funding acquisition [supporting], Methodology [supporting], Writing—review & editing [supporting]), Valerie Fournier (Conceptualization [lead], Funding acquisition [lead], Methodology [supporting], Supervision [lead], Writing—review & editing [supporting]), and Pierre Giovenazzo (Conceptualization [lead], Funding acquisition [lead], Methodology [supporting], Project administration [lead], Supervision [lead], Writing—review & editing [supporting])

Supplementary Material

Supplementary material is available at Journal of Economic Entomology online.

Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada-Alliance Grants Program under grant ALLRP # 561308-20; the Natural Sciences and Engineering Research Council of Canada Discovery Grants Program under Grant RGPIN #2019-05843; Centre de Recherche en Sciences Animales de Deschambault (CRSAD); Syndicat des Producteurs de Bleuets du Québec (SPBQ); Les Apiculteurs et Apicultrices du Québec (AADQ); Quebec Department of Agriculture, Fisheries and Food (MAPAQ); and BioBest.

Conflicts of Interest

The authors declare no conflict of interest.

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.