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Article

Poor Air Quality Is Linked to Stress in Honeybees and Can Be Compounded by the Presence of Disease

by
Christopher Mayack
1,
Sarah E. Cook
2,3,
Bernardo D. Niño
1,
Laura Rivera
1,4,
Elina L. Niño
4 and
Arathi Seshadri
1,*
1
USDA/ARS/WRRC, Invasive Species and Pollinator Health Research Unit, Davis, CA 95616, USA
2
SpecialtyVETPATH, 3450 16th Ave. W. Ste 303, Seattle, WA 98119, USA
3
Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, 944 Garrod Drive, Davis, CA 95616, USA
4
Department of Entomology and Nematology, University of California, 1 Shields Avenue, Davis, CA 95616, USA
*
Author to whom correspondence should be addressed.
Insects 2023, 14(8), 689; https://doi.org/10.3390/insects14080689
Submission received: 19 May 2023 / Revised: 21 July 2023 / Accepted: 2 August 2023 / Published: 4 August 2023
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Abstract

:

Simple Summary

Climate change is associated with warmer and drier weather on average in central California. At the same time, honeybees are being transported from all over the United States to California for the completion of pollination services that ensure increased yields for a variety of crops, resulting in bees experiencing ambient abiotic stressors. Higher temperatures can reduce air quality, further exacerbating honeybee health challenges. We investigated the relationship between higher daily temperatures and poor air quality and demonstrated associations with incidences of pests (e.g., Varroa mites) and pathogens (e.g., Nosema ceranae). We also correlated the expression of genes linked to immune functioning, oxidative stress, and buffering against temperature and air quality stressors. High daily temperatures are associated with poorer air quality, and they are both associated with lowered immune system functions and increased oxidative stress protection against pests and diseases. Higher Varroa mite loads are correlated with the lower potential ability of honey bees to buffer against temperature stress. Our study provides insights into interactions between climate change-related abiotic stressors and their relation to biotic stressors, which underlie a decline in honeybee health.

Abstract

Climate change-related extreme weather events have manifested in the western United States as warmer and drier conditions with an increased risk of wildfires. Honeybees, essential for crop pollination in California, are at the center of these extreme weather events. We associated the maximum daily temperature and air quality index values with the performance of colonies placed in wildfire-prone areas and determined the impact of these abiotic stressors on gene expression and histopathology. Our results indicate that poor air quality was associated with higher maximum daily temperatures and a lower gene expression level of Prophenoloxidase (ProPO), which is tied to immune system strength; however, a higher gene expression level of Vitellogenin (Vg) is tied to oxidative stress. There was a positive relationship between Varroa mites and N. ceranae pathogen loads, and a negative correlation between Varroa mites and Heat Shock Protein 70 (HSP70) gene expression, suggesting the limited ability of mite-infested colonies to buffer against extreme temperatures. Histological analyses did not reveal overt signs of interaction between pathology and abiotic stressors, but N. ceranae infections were evident. Our study provides insights into interactions between abiotic stressors, their relation to common biotic stressors, and the expression of genes related to immunity and oxidative stress in bees.

1. Introduction

Ongoing changes to climatic conditions are resulting in frequent extreme temperature fluctuations and a rise in weather-related natural disasters, which, in turn, affect ecosystems, and the species within [1]. Wildfires, temperature extremes, drought, and flooding in California, are now more prevalent than in the past and are predicted to worsen in the future [2]. In general, climate change is expected to affect global health, distribution, and the abundance of organisms [3,4,5], and pollinators are no exception to this [6,7]. The western honeybee (Apis mellifera) is crucial for pollination in natural and agricultural ecosystems, with annual economic contributions estimated to be over $200 billion globally [8,9] and over $18 billion in the United States [10,11]. In recent years, there has been an ongoing decline in bee health attributed mainly to pests and pathogens, poor nutrition, and pesticide exposure [12,13,14,15,16], and bees are facing further challenges in relation to rapidly changing weather patterns. A global decline in pollinator biodiversity has at least in part been attributed to the effects of climate change [7,17]. For climates like California, which are amenable for agriculture production, climate-change-mediated warmer temperatures impact insect pollinators and, thus, their pollination efficiency, eventually affecting agricultural production [18]. Beekeepers have raised concerns and reported adverse consequences related to severe weather events, including the loss of colonies to wildfires, weakened colonies from a scarcity of nectar and pollen due to changing phenology and bloom periods [19,20], decreased honey production, and greater pathogen loads exacerbating ongoing challenges to pollinator health [21,22].
Another direct consequence of extreme weather events [23] in relation to climate change is poor air quality which can have a direct negative impact on honeybee health [24,25]. Honeybees have previously been used as sentinels to monitor environmental pollution [26,27], but little attention has been given to the detrimental impacts of pollution on bees themselves. As beekeepers from across the United States bring their colonies to California for crop pollination, honeybee colonies are substantially exposed to the adversities of climate-related extremes in California, even if only for part of the year [28,29]. In addition, most queen honeybees and colonies in the rest of the country come from California, requiring the beekeeping and queen-rearing industries to maintain robust colonies throughout the year. While climate change by itself is likely a substantial stressor on honeybee health, its interactions with other existing biotic stressors can further compound these impacts, though, at this time, the nature of these interactions is not fully clear.
Of the several biotic stressors that affect honeybees, parasitic mites, such as Varroa destructor, affect brood and adult bees, and chronic infections from the pathogen, Nosema ceranae, affect adult bees and are critically important. The challenges from these biotic stressors may be compounded as bees adapt to abiotic stressors resulting from changing weather patterns. Our goal in this study is to assess the relationship between two main abiotic factors—temperature and air quality—and explore their relation to the two above-mentioned biotic stressors. Specifically, we investigated how daily maximum temperatures and poor air quality relate to each other and to N. ceranae and V. destructor: the chronic biotic stressors. While N. ceranae is relatively less virulent, it suppresses the immune system and causes energetic stress and metabolic dysregulation that could potentially be compounded by other stressors [12,16,30,31]. Varroa mites, on the other hand, are central to declines in bee health as they nutritionally deplete the honeybee by directly feeding on them [32] and vector more virulent strains of viruses [33].
We then assayed the gene expression of stress biomarkers that are activated in response to different stress pathways with the goal of gaining mechanistic insight into the interactions between abiotic and biotic stressors as they impact honeybee health. We measured the gene expression for Heat Shock Protein 70 (HSP70), a general marker for the ability of bees to buffer against stress from extreme temperature changes [34], Prophenoloxidase (ProPO), a marker for immune upregulation in response to disease stress [35] and Vitellogenin (Vg), a general physiological stress marker known to be a master regulator with ties to oxidative stress, immune functioning, development, and metabolism [36]. We further assayed individual bees for morphological signs (histologic imaging) of stress and for pathologic lesions of N. ceranae incidence as secondary measures of the potential adverse impacts of temperature and poor air quality. Histologic imaging, which involves the microscopic evaluation of tissues and cells, has more recently gained the attention of honeybee researchers [37,38,39,40,41,42].

2. Materials and Methods

2.1. Sample and Environmental Data Collection

This study was conducted from June to November of 2021 with 12 standard Langstroth hives consisting of a total of 18 frames and a 5.68 L (1.5 gal) feeder. The hives were equalized in strength and were placed near areas that were known to be prone to wildfires, including six different locations in the Napa, Yolo, and Solano counties of central California (Figure 1). Hives in all the locations except in sites 3 and 4 (treatment-free operations) received recommended best hive practices [43,44]. All the hives were fed weekly with 3.79 L (1 gal) of a pro-sweet liquid feed (Mann Lake Ltd., Mann Lake) and ultra-bee pollen patty (Mann Lake Ltd., Mann Lake). As they returned from foraging trips, approximately 150 forager bees were collected from each colony at the entrance to the hive using an insect vacuum and were then placed in 50 mL Falcon tubes labeled by the date, hive number, and location. A total of approximately 150 in-hive bees were collected on the same day by rolling bees down a central capped brood frame of the hive into a separate 50 mL Falcon tube. Monthly collections were completed, samples were stored on dry ice after field collections and then transferred to a −80 °C freezer until further analyses. From the weather station closest to each apiary location, historical maximum daily temperatures for each sampling date were retrieved from the Farmer’s Almanac (https://www.farmersalmanac.com/long-range-weather-forecast, accessed on 15 February 2023) and the air quality index (AQI) for each sampling date was noted from AirNow.gov (https://www.airnow.gov/, accessed on 15 February 2023).

2.2. Measuring Colony Performance Metrics

The monthly, detailed assessments included recording the Frames of Bees (FOB), the presence of a laying queen as indicated by the presence of eggs, and measuring the amount of brood, pollen, nectar, honey, and the amount of empty space on a frame, on a scale of 0–10. Varroa mite loads for each colony were also measured via the standard alcohol wash method [45]. Approximately 300 bees were scooped with a cup from a brood frame from each hive to estimate the total number of Varroa mites in the hive. Colonies with mite levels at over 3% of the threshold received mite control treatments [46]. The frames of bees (FOB) measure is an effective method to quickly estimate the population size of adult bees in colonies. This was completed by counting the top and bottom of frames covered with bees in the hive. In-hive bees, not on the central part of the frames but present on the top of frames and at the bottom of the hive box, were estimated as one more FOB and were added to the total for each hive, providing an estimate of the adult bee strength. By contrast, the number of adult bees was assessed by measuring how much area the bees occupied on the side of each frame in the hive.

2.3. Sample Preparation for Molecular Analysis

In-hive and forager bees were placed in separate 50 mL falcon tubes and were thawed, macerated, and homogenized in 5 mL of nuclease-free water (VWR, Radnor, PA, USA) per 30 collected bees, using a 50 mL disposable tissue grinder (VWR, Radnor, PA, USA). The bee homogenate was then aliquoted as a 200 µL volume into three different 1.5 mL microcentrifuge tubes, which were stored in a −80 °C freezer until further analysis.

2.4. Nosema qPCR Quantification

DNA extraction to quantify Nosema infection loads was adapted from [12]. Briefly, 300 µL of the HBRC buffer was added to 200 μL of bee homogenate, and this was treated with 4 µL (20 mg/mL) of proteinase K for 3 h at 60 °C. A total of 300 µL of 1:1 phenol-chloroform was added, and this was centrifuged at 13,000 rpm for 5 min. This step was repeated once, and the supernatant was then added to a 1.5 mL microcentrifuge tube that contained 300 μL of chloroform. This was then centrifuged at 13,000 rpm for 5 min. Finally, the supernatant was added to 30 μL of a 3 M sodium acetate (pH of 5.2) along with 600 μL of 95% ethanol to precipitate the DNA overnight at −20 °C. The amount and purity of the extracted DNA were assessed using a Thermo Fisher Scientific NanoDrop spectrophotometer (Waltham, MA, USA). Each DNA extraction was diluted to 10 ng/μL for the qPCR template DNA. Methods for the quantification of Nosema and the identification of the species from a melt curve analysis were performed following [47]. The 25 μL duplex qPCR reaction consisted of 0.4 uL of ROX (reference dye), 12.5 μL of 2x GoTaq Promega qPCR master mix, a 175 nM F-NAPIS primer (0.4375 μL), a 175 nM R-NAPIS primer (0.4375 μL), 350 nM F-NCERANAE (0.875 ul), 350 nM R-NCERANAE (0.875 μL) (Table S1), 1 μL of template DNA, and 8.475 μL of nuclease-free water. The thermocycler settings were as follows: a 10 min denaturing period at 95 °C, 40 cycles of 30 s denaturing at 95 °C, 30 s annealing at 60 °C, 30 s extension at 72 °C, and a 5 min extension period at 72 °C. The melt curve analysis was as follows: 1 min at 95 °C and 30 s at 55 °C with 40 successive 30-s increases of 1 °C. Each run contained N. apis and N. ceranae positive control as well as a negative control. The samples were run in the Biorad CFX384 qPCR thermocycler (Hercules, CA, USA) in technical duplicates. If the standard deviation between duplicates was greater than one, the samples were re-run. The amount of Nosema DNA that was quantified was converted to the total number of Nosema copies using the standard curves provided [47].

2.5. Gene Expression Analysis

All primer set sequence information used for gene expression analysis can be found in Table S1. Methods for the RNA extraction of other gene expression analyses were adapted from [48]. Briefly, a 5 mm steel bead was added to 200 µL of the bee homogenate in a 2 mL Eppendorf safe-lock microcentrifuge tube. The top of the tube was wrapped in parafilm, and the tube was placed in the BeadBlaster™ 24 (Benchmark Scientific, Sayreville, NJ, USA) and was set to 4:00 m/s for 1 cycle with a 15 s interval, and the duration of the run time was 30 s. A 1% β-mercaptoethanol RLT buffer solution was added to the samples; the RLT buffer solution was part of the Qiagen RNeasy Plant Mini Kit (Germantown, MD, USA). The mixture was vortexed for 30 s. Then, the RNA extraction proceeded by following the manufacturer’s instructions of the Qiagen RNeasy Plant Mini Kit (Germantown, MD, USA) with an Invitrogen™ TURBO™ DNase kit (Waltham, MS, USA) on column treatment to remove any genomic DNA contamination. The amount and purity of the extracted RNA were assessed using a Thermo Fisher Scientific NanoDrop spectrophotometer (Waltham, MA, USA).
A blend of random hexamers and an oligo dT SensiFAST cDNA Synthesis Kit (Newtown, OH, USA) was used following the manufacturer’s instructions to generate cDNA from the extracted RNA standardized to 700 ng. A 20 µL cDNA synthesis reaction was carried out involving 4 µL of a 5x TransAmp Buffer, 1 µL of reverse transcriptase, and 15 µL of extracted RNA. The relative gene expression of a total of three targets, Heat Shock Protein 70 (HSP70), Vitellogenin (Vg), and Prophenoloxidase (ProPO), were quantified relative to the reference gene Ribosomal Protein S5 (RpS5). The following qPCR protocols were used for each target. For the quantification of the Vg and RpS5 expression, the final volume of the reaction was 10 µL which consisted of 5 µL of qPCR 2x Promega (Madison, WI, USA) master mix (no ROX), 0.2 µL (10 µM) of either the Vg-F or Vg-R primers or the RpS5-F and RpS5-R primers, respectively, including 2 µL of template cDNA, and 2.6 µL of nuclease-free water. The qPCR thermocycler program was: 95 °C for 5 min, 45 cycles of 95 °C for 10 s, and 60 °C for 20 s, with a final extension step of 72 °C for 30 s [49]. For ProPO analysis, the following 20 μL of the qPCR reaction was used: 10 μL of 2x qPCR master mix, 1 μL (10 μM) of each F-ProPO and R-ProPO primer, 6.4 μL nuclease-free water, 0.4 μL of ROX Reference dye II, and 1 μL of template cDNA. The following qPCR thermocycler program was used for ProPO: 10 min at 95 °C, followed by 40 cycles of 20 s at 95 °C, 20 s at 55 °C, and a final extension step of 30 s at 72 °C [35]. For HSP70, the following 25 μL qPCR reaction was used: 12.5 μL of a 2x qPCR master mix (no ROX), 0.5 μL (10 µM) of the F-HSP70 primer, 0.5 μL of the R-HSP70 primer, 9.5 μL of nuclease-free water, and 2 μL of template cDNA. The qPCR thermocycler program consisted of: 95 °C for 15 min, 40 cycles at 95 °C for 20 s, 58 °C for 30 s, and a final extension step of 72 °C for 30 s [34]. Each run consisted of technical duplicates and was run in the Biorad CFX384 qPCR thermocycler (Hercules, CA, USA). If the standard deviation for the reference gene was greater than one, then the samples were re-run, and if for each target it was greater than two, the samples were re-run. The relative gene expression was calculated using the delta–delta Ct method.

2.6. Histology Methods

Methods were adapted from Cook et al. (In press). Briefly, honeybees were collected and stored in 10% neutral buffered formalin (NBF) and were fixed for a minimum of 24 h. The head, thorax, and abdomen were separated using spring scissors (World Precision Instruments, Sarasota, FL, USA) or a steel surgical blade (Miltex, NY, USA). The head and thorax were subsequently sectioned transversely, and the abdomen was sectioned in a sagittal orientation along the midline using a carbon steel surgical blade (Miltex, NY, USA). Blades were replaced with at least every three samples to prevent dulling, and all tissues were placed with their cut surface down in either one or two tissue cassettes (Tissue-Tek). All cassettes were stored in 10% NBF until further routine laboratory processing, including routine paraffin embedding, sectioning at 4 µM sections, mounting onto glass slides, and stained with hematoxylin and eosin (H&E). Two consecutive sections of all three body segments were mounted onto a single glass slide for light microscopic evaluation and subsequent image analysis.
Based on the results of molecular analysis for gene expression, targeted groups were evaluated via light microscopy and image analysis. For microscopic evaluation, all body segments and tissues were assessed, and any lesions were documented. For image analysis, the total surface area for hypopharyngeal glands located dorsal to the central nervous system within the head section was calculated, as was the mandibular gland surface area. For mandibular glands, if both left and right glands were in the plane section of the slide, then both sides were measured and divided by two to obtain an average for one side. If only one side was in the section, then just that half was measured. The surface area of the glands was measured using Adobe Photoshop (v 24.1.1).
Finally, each bee was histologically evaluated for Nosemosis to compare the prevalence diagnosed histologically with the molecular quantification of Nosema. Bees were sampled monthly from June to November 2021. Six sites were sampled from June to September, and 10 nurse and 10 forager bees from each site were processed (a total of 120 bees for each time point). One site was discontinued in September as the colonies died, and the remaining five sites were sampled similarly in October and November (100 bees for each time point).

2.7. Statistical Analyses

All statistical analyses were carried out using JMP Pro v. 16 (SAS Institute, Cary, NC, USA) or GraphPad Prism (San Diego, CA, USA). Data visualization was carried out using GraphPad Prism (San Diego, CA, USA). All numeric variables (N. ceranae load, air quality index, maximum daily temperature, the normalized gene expression of HSP70, ProPO, and Vg, FOB, Varroa mite counts, adult bees, the amount of the brood, the amount of honey, the amount of nectar, the amount of pollen, and the amount of empty space in the hive) did not follow a normal distribution. All count data were over-dispersed. Therefore, a generalized linear negative binomial regression model was used where the date of collection, the apiary sampled, and whether the bees collected were foragers or in-hive bees were included in the full model as random effects. The apiary, date, and age class of the bee had significant random effects when analyzing the gene expression of biomarkers for N. ceranae and Varroa mite loads. These random effect variables were then removed from the models due to the resultant lower AIC values when analyzing relationships between diseases and gene expression markers.

2.7.1. Abiotic Stressor Analysis

Abiotic stressors (maximum daily temperature and air quality) in relationship with gene expression markers and diseases were accounted for in a multivariate Spearman rank correlation analysis.

2.7.2. Biotic Stressor Analysis

The N. ceranae load was analyzed within foragers and in-hive bees in relation to the other numeric variables using a negative binomial generalized linear regression. For N. ceranae and Varroa mites, the date and apiary had significant random effects on the previous negative binomial regression analysis. Therefore, N. ceranae and Varroa mite loads were further analyzed across apiaries and time with Wilcoxon pairwise comparisons after applying relevant post hoc adjustments for multiple comparisons.

2.7.3. Gene Expression Analysis

For each gene expression target (Vg, ProPO, and HSP70), the date had a significant random effect in the previous negative binomial regression analysis. Therefore, each gene target was further analyzed across time with Wilcoxon pairwise comparisons.

2.7.4. Colony Detailed Assessment Correlation Analysis

Lastly, a multivariate Spearman rank correlation analysis was performed to determine the association between the detailed assessment measurements (FOB, number of adult bees, amounts of honey, nectar, pollen, and empty hive space), environmental measures (air quality index, and maximum daily temperature), the disease and pest loads (N. ceranae and Varroa mites), and gene expression measures (ProPO, HSP70, and Vg).

2.7.5. Histological Analyses of Bees

For the quantitative analysis of the histologically determined surface area of hypopharyngeal glands and mandibular glands, Student’s t-tests were performed for each comparison group using Prism 9 (GraphPad, v 9.4.1).

3. Results

3.1. Abiotic Stressors—Temperature and Air Quality Index (AQI)

The maximum daily temperature and AQI values were found to be positively correlated with each other (ρ = 0.28, df = 63, p = 0.027; Figure 2) and associated with the expression of ProPO and Vg genes (Figure 2). Forager bees that were exposed to poorer air quality had a negative association with ProPO gene expression (ρ = −0.2839, df = 63, p = 0.023; Figure 2A). Poor air quality was also positively correlated with the expression of the Vg gene (ρ = 0.26, df = 63, p = 0.038; Figure 2B). Among in-hive bees, there was a positive relationship between HSP70 and ProPO gene expression (ρ = 0.60, df = 64, p < 0.001) (Table S2). Among foragers, there was a positive relationship between the expression levels of the HSP70 gene and pollen stored in the hive. Adult bees were positively correlated with FOB and the maximum daily temperature (Table S3). Air quality index values varied more across apiaries in comparison to the maximum daily temperatures over the course of the study (Figure S1).

3.2. Biotic Stressors—N. ceranae and Varroa:

The disease and pest incidences, N. ceranae and Varroa mites, were related to the gene expression levels of ProPO and HSP70 (Figure 3). Loads of both stressors increased later in the season, with the highest levels recorded in the fall. Across foragers and in-hive bees, there was a positive relationship between the Varroa mite and N. ceranae loads (Negative binomial regression: χ2 = 4.98, df = 62, p = 0.026; χ2 = 5.34, df = 64, p = 0.021; Figure 3). The highest levels of Varroa and N. ceranae were found in two apiaries (3 and 4) that were near apiaries that did not receive any hive management measures to control these stressors (Figure 4 and Figure 5).

3.2.1. N. ceranae Analysis

The overall prevalence of N. ceranae infections across apiaries and all sampling dates was 72.31%. The pattern of N. ceranae loads in forager bees across the collection season is shown in Figure 4A, with loads in November appearing significantly higher than in the earlier months. The N. ceranae loads were the highest from sites 3 and 4 in comparison to the other apiary locations (Figure 4B). For in-hive bees, there was a similar trend; the highest loads were found in November in comparison to the other sampling months, and the highest levels of N. ceranae were found in sites 3 and 4 (Figure 4A,B). There were also significantly higher levels of N. ceranae in forager bees versus in-hive bees (negative binomial regression: χ21,129 = 7.53, p = 0.0061; Figure 4C).

3.2.2. Varroa Mite Analysis

The overall prevalence of Varroa mite infestations across apiaries and all sampling dates was 60.00%. There were significantly higher Varroa mite loads in October and November in comparison to June. Across apiaries, there were significantly high Varroa mite loads in sites 3 and 4 in comparison to the rest (Figure 5).

3.3. Gene Expression Analysis

For in-hive and forager bees, there were higher levels of the HSP70 gene expression during the summer months. The highest Vg gene expression was in June and September for in-hive and forager bees. There were significantly lower levels of ProPO gene expression in September than in October and November for foragers, but in-hive bees showed higher levels in June than in the other months (Figure S2). Lastly, there were no significant differences in gene expression across apiaries for both foragers and in-hive bees. N. ceranae loads in forager bees were positively associated with ProPO gene expression (N. ceranae: ρ = 0.27, df = 63, p = 0.03). Varroa mite loads were positively associated with ProPO gene expression within forager bees (Negative binomial regression: χ2 = 6.50, df = 62, p = 0.011). However, there was a negative relationship between Varroa mite loads and forager bee HSP70 gene expression (χ2 = 4.53, df = 62, p = 0.033; Figure 3). Among foragers, there was a positive relationship between HSP70 gene expression and the amount of pollen stored in the hive. Among in-hive bees, there was a positive relationship between HSP70 and ProPO gene expression (ρ = 0.60, df = 64, p < 0.001; Figure 3).

3.4. Colony Detailed Assessment Correlation Analysis

Adult bees, FOB, brood, honey, and nectar stores (uncapped honey) were all positively correlated with one another and negatively correlated with empty space in the hive. Pollen was positively correlated with adult bees, the frames of bees, and brood. N. ceranae loads in forager bees were positively correlated only with nectar stores and the number of adult bees in the hive, while it was negatively correlated with the amount of empty space in the hive. There was also a positive correlation between the amount of honey and Varroa mite loads. There was a negative association between the maximum daily temperature and Varroa mites. All correlations and individual statistical values can be found in Table S3.

3.5. Histological Analyses of Bees

Based on the results of molecular findings and our interest in the effects of air quality and temperature on honey bee health and stress, the two hives with the greatest differences molecularly were histologically assessed for pathologic lesions. No trends or lesions were identified to correlate with air quality or temperature. Similarly, based on the results of the molecular findings, bees from two hives with the largest difference in gene expression, pest and disease loads, and climate variables were measured for their hypopharyngeal and mandibular gland size across each category (e.g., AQI, temperature, Varroa and N. ceranae loads, levels ProPO, Vg, HSP70). No trends or significant differences in gland sizes were identified for any group comparison (e.g., Figure 6). Finally, Nosemosis incidence was identified histologically and showed trends toward a high incidence in the October and November samples for two of the sites, while in the remaining sites, the pathogen was low or absent (Figure 7 and Figure 8).

4. Discussion

Our results demonstrate relationships between climate change-mediated abiotic stressors (daily maximum temperature and air quality) on honeybee health parameters, including common biotic stressors—N. ceranae and Varroa mites—and the expression of different biomarker genes that are indicators of honeybee health. The positive relationship between daily maximum temperatures and poor air quality could lead to an increase in the susceptibility of forager bees to most likely be infected by a compromised immune system. Here, we found an increase in ProPO gene expression associated with high disease loads, yet with poorer air quality, there was an associated decrease in the ProPO gene expression. This enzyme is activated as part of a general innate immune response [50]; therefore, if its expression cannot be upregulated due to poor air quality, then it results in increased susceptibility of bees to particular diseases, such as viruses transmitted by Varroa mites. This relationship has been reiterated by a high correlation between the ProPO gene expression and Phenoloxidase activity across the development and castes of honeybees infested with Varroa mites [35]. In general, ProPO enzyme activity serves as one of the first lines of defense against diseases [50] in honeybees, and, in our study, this line of defense appears to be compromised with exposure to poor air quality.
Furthermore, Varroa mites were found to be correlated with a potentially lowered ability of foragers to buffer against high temperatures. The lowered expression of HSP70 genes in our analysis suggests exacerbated challenges for honeybees if atmospheric temperatures continue to move toward the extremes in the future. Heat shock proteins were highly conserved across organisms and upregulated in response to temperature and other stressors. These HSP proteins chaperone the assembly, disassembly, folding, and unfolding of protein complexes so that their function can be preserved under stress [51]. The role of these proteins could become more critical as predicted temperature extremes become a reality in the future. While it is likely that ample pollen stores, as in our study hives, may facilitate an increase in the expression of HSP70 gene expression, temperature stress, when combined with drought, may reduce pollen production in plants, thus limiting pollen availability for foraging bees [52,53]. Thus, the supplemental feeding of colonies could become critical for bees to retain their ability to combat abiotic stressors with appropriate responses at the physiological level, and this adds costs to hive management and increases the risks of hive losses.
Our results on the colony performance metrics indicate that the presence of N. ceranae or Varroa mites is not necessarily associated with a weak colony but instead may be dependent on other factors related to abiotic parameters, implying that colony-level indicators might not always be reliable. These discrepancies could relate to the differences in the seasonality of N. ceranae infections, where spore loads peak in the spring and are lowest in the fall [54], but whether these seasonal variations are linked to air temperatures is not fully understood. In our study, we did not measure N. ceranae loads in the spring, but we detected the highest levels in the fall and relatively stable levels throughout the rest of the beekeeping season, suggesting a chronic colony-level infection with this pathogen. The constant higher temperatures and poorer air quality year-round in the Central Valley of California during the study years might suppress the immune system of honeybees, as indicated by the lowering of the ProPO gene expression. This lowered expression could explain the higher disease loads observed year-round in this climate in comparison to more temperate climates, where N. ceranae levels are more seasonal [54]. The seasonal pattern we documented for N. ceranae here is similar to that reported in Spain, where N. ceranae is known to be particularly virulent [55]. Spain is a location with a Mediterranean climate like our study region, and N. ceranae infections in Spain are found year-round in colonies with a lack of seasonality for this pathogen [56]. Thus, our findings on disease loads and their relation to high daily temperatures and poor air quality have implications for other regions around the world that also have prevalent beekeeping operations and are experiencing climate change-related stressors resulting in drier and warmer weather patterns, as in our study area [57].
The higher amount of N. ceranae in forager bees versus in-hive bees is also supported by previous findings [58]. This difference is interesting to note, however, because forager bees are also subjected to more extreme climate conditions while foraging [59]. It is of interest to note that we did not detect N. apis in any of the samples based on the melt curve analysis, supporting the notion that N. ceranae is displacing N. apis throughout the world [60]. Our results also showed that poor air quality is associated with a decrease in ProPO gene expression in forager bees, suggesting that these bees, in an unfavorable environment, may have a decreased ability to combat infections and mount a strong immune response. Poor air quality showed a positive relationship with Vg expression in forager bees and increased Vg through upregulations, which is known to protect workers from oxidative stress, suggesting that poor air quality further stresses honeybee foragers. However, this trend does not hold in nurse bees which may be because they typically already have high levels of Vg expression [61]. The nurse bees only exhibit a positive correlation between ProPO and HSP70 gene expression and not with any of the other stressors. These physiological changes in forager bees from poor air quality exposure may be further exacerbated by maximum daily temperatures, which have been shown to have a strong positive association with poor air quality [62]. Our results show associations between abiotic stressors and gene expression and lend support to the increase in challenges that bees are likely to face if there is a further decrease in air quality in this region especially relating to smoke from wildfires.
Despite evidence that Varroa is known to suppress the immune system in newly emerged bees [35], we found a positive relationship with ProPO gene expression in forager bees. Varroa mites and N. ceranae were also positively correlated in this study. Therefore, this and other diseases present in Varroa-infested hives may likely cause a net increase in the innate immune response of forager bees. Supporting this notion, Varroa is known to vector viruses such as the Deformed Wing Virus (DWV) [33], which by itself has been shown to increase ProPO expression [63]. Therefore, there appears to be a net increase in the expression of the immune response ProPO gene in forager bees with a heavy disease burden. Thus, ProPO could serve as a dependable immune marker gene that reflects the ability of bees to fight and fend off potential infections.
Climate change for our study region is projected to lead to sustained increases in maximum daily temperatures in the future [2], which is associated with poorer air quality. Thus, with Varroa mites being negatively correlated with HSP70 gene expression in forager bees, there is a strong likelihood of further hampering the ability of bees to tolerate extreme temperatures [34]. Furthermore, larvae without Varroa mites were found to have significantly lower HSP70 gene expression levels [64]; however, in pupae, Varroa mite infestation caused a decrease in HSP70 gene expression [65]. Varroa is known to vector virulent viruses, including fungal diseases such as stone brood, feed on fat body and hemolymph, as well as suppress the immune system [32,33,48,66] and, thus, is a major factor in the decline of bee health. Ongoing changes to climate could further augment the stresses caused by this parasitic mite. A closer look at our result showing a negative relationship between high temperatures and the Varroa mite loads in hives is indicative of some cautious optimism that the higher temperatures predicted in the future for this region might have benefits, as previous research demonstrated that Varroa mites were indeed sensitive to higher temperatures [67]. Bee heaters have been proposed as an effective method for killing and managing Varroa mite loads. However, this comes with several caveats, depending on the levels of high temperatures that honeybees can tolerate. However, it can be noted here that there are several heat shock proteins involved in proteostasis, and our study did not measure the capacity of bees to handle heat stress. Bees are known to have a slightly higher tolerance to high temperatures compared to Varroa mites, but it is also likely that this small beneficial impact could become minimal over an extended time as the parasite adapts to changing environmental conditions [68].
Histological analyses are gradually regaining center stage in research related to honeybee health, but our analyses show that histologic imaging may not be reliable to measure the impact of abiotic stressors on honeybees. While the pathological damage from N. ceranae infections is dependable and the loads detected followed similar trends to the molecular quantification analysis, the relationships between high temperature and poor air quality with expression levels of stress genes were weak. These abiotic stressors did not result in immediate signs of pathological damage to hypopharyngeal or mandibular glands that are indicators of honeybee health [42].

5. Conclusions

In summary, poor air quality is a serious environmental stressor impacting honeybees [24]. Air pollutants mask the floral odor receptors of honeybee antennae [25], limiting foraging success and colony nutrition. Our results demonstrate the relationship between air quality and daily maximum temperatures together can compound the impact of other stressors and the ability of bees to buffer against challenges.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/insects14080689/s1, Figure S1: The maximum daily temperatures measured at the closest weather station to each apiary and the corresponding air quality index value from each weather station; Figure S2: A box plot representation of the medians and inter-quartile ranges of expression levels of (A) Heat Shock Protein (HSP70), (B) Vitellogenin (Vg), and (C) Prophenoloxidase (ProPO) genes across the months the hives were sampled for forager and in-hive bees. Table S1: Primers for Nosema quantification as well as for each gene expression target.; Table S2: Spearman rank correlation values showing the relationship between maximum air temperatures and Air Quality Index (AQI) values. Table S3: The statistical results from a multivariate spearman rank analysis between Variables X and Y.

Author Contributions

Conceptualization, A.S., S.E.C., B.D.N. and E.L.N.; methodology, A.S., C.M., S.E.C., L.R., B.D.N. and E.L.N.; software, A.S., E.L.N., C.M. and S.E.C.; validation, C.M. and S.E.C.; formal analysis, S.E.C. and C.M.; investigation, C.M., S.E.C., B.D.N. and L.R.; resources, A.S. and E.L.N.; data curation, C.M. and L.R.; writing—original draft preparation, C.M. and A.S.; writing—review and editing, A.S., L.R., S.E.C., B.D.N. and E.L.N.; visualization, C.M. and S.E.C.; supervision, A.S. and E.L.N.; project administration, A.S. and E.L.N.; funding acquisition, A.S. and E.L.N. All authors have read and agreed to the published version of the manuscript.

Funding

Funding for this research was supported by USDA-ARS 2030-21000-055-000-D funds to BDN, CM, LR, and AHS, and USDA-ARS cooperative agreement 58-2030-0-040 and various other donors to ELN.

Data Availability Statement

The data collected corresponding to this project have been made publicly available through the open science framework and can be found at DOI 10.17605/OSF.IO/MC73D.

Acknowledgments

The statements made in the article represent the author’s views and should not be interpreted as an endorsement from their respective employers or the funding agencies. The mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply a recommendation or endorsement by the University of California, Davis, or the US Department of Agriculture (USDA). USDA is an equal opportunity provider, employer, and lender.

Conflicts of Interest

The authors declare no potential conflict of interest with respect to the research, authorship, and/or publication of this article.

References

  1. Ebi, K.L.; Vanos, J.; Baldwin, J.W.; Bell, J.E.; Hondula, D.M.; Errett, N.A.; Hayes, K.; Reid, C.E.; Saha, S.; Spector, J.; et al. Extreme Weather and Climate Change: Population Health and Health System Implications. Annu. Rev. Public Health 2021, 42, 293–315. [Google Scholar] [CrossRef]
  2. Rapacciuolo, G.; Maher, S.P.; Schneider, A.C.; Hammond, T.T.; Jabis, M.D.; Walsh, R.E.; Iknayan, K.J.; Walden, G.K.; Oldfather, M.F.; Ackerly, D.D.; et al. Beyond a warming fingerprint: Individualistic biogeographic responses to heterogeneous climate change in California. Glob. Chang. Biol. 2014, 20, 2841–2855. [Google Scholar] [CrossRef] [Green Version]
  3. Bohan, D.A.; Richter, A.; Bane, M.; Therond, O.; Pocock, M.J. Farmer-led agroecology for biodiversity with climate change. Trends Ecol. Evol. 2022, 37, 927–930. [Google Scholar] [CrossRef]
  4. Marshman, J.; Blay-Palmer, A.; Landman, K. Anthropocene Crisis: Climate Change, Pollinators, and Food Security. Environments 2019, 6, 22. [Google Scholar] [CrossRef] [Green Version]
  5. Parmesan, C. Ecological and Evolutionary Responses to Recent Climate Change. Annu. Rev. Ecol. Evol. Syst. 2006, 37, 637–669. [Google Scholar] [CrossRef] [Green Version]
  6. Stange, E.E.; Ayres, M.P. Climate Change Impacts: Insects. In Encyclopedia of Life Sciences; Wiley: Hoboken, NJ, USA, 2010. [Google Scholar]
  7. Potts, S.G.; Biesmeijer, J.C.; Kremen, C.; Neumann, P.; Schweiger, O.; Kunin, W.E. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 2010, 25, 345–353. [Google Scholar] [CrossRef] [PubMed]
  8. Gallai, N.; Salles, J.-M.; Settele, J.; Vaissière, B.E. Economic valuation of the vulnerability of world agriculture confronted with pollinator decline. Ecol. Econ. 2009, 68, 810–821. [Google Scholar] [CrossRef]
  9. Hung, K.-L.J.; Kingston, J.M.; Albrecht, M.; Holway, D.A.; Kohn, J.R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. R. Soc. B Boil. Sci. 2018, 285, 20172140. [Google Scholar] [CrossRef] [Green Version]
  10. Reilly, J.R.; Artz, D.R.; Biddinger, D.; Bobiwash, K.; Boyle, N.K.; Brittain, C.; Brokaw, J.; Campbell, J.W.; Daniels, J.; Elle, E.; et al. Crop production in the USA is frequently limited by a lack of pollinators. Proc. R. Soc. B Boil. Sci. 2020, 287, 20200922. [Google Scholar] [CrossRef]
  11. Calderone, N.W. Insect Pollinated Crops, Insect Pollinators and US Agriculture: Trend Analysis of Aggregate Data for the Period 1992–2009. PLoS ONE 2012, 7, e37235. [Google Scholar] [CrossRef] [Green Version]
  12. Mayack, C.; Macherone, A.; Zaki, A.G.; Filiztekin, E.; Özkazanç, B.; Koperly, Y.; Schick, S.J.; Eppley, E.J.; Deb, M.; Ambiel, N.; et al. Environmental exposures associated with honey bee health. Chemosphere 2022, 286, 131948. [Google Scholar] [CrossRef] [PubMed]
  13. Goulson, D.; Nicholls, E.; Botias, C.; Rotheray, E.L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 2015, 347, 1255957. [Google Scholar] [CrossRef] [PubMed]
  14. Naug, D. Nutritional stress due to habitat loss may explain recent honeybee colony collapses. Biol. Conserv. 2009, 142, 2369–2372. [Google Scholar] [CrossRef]
  15. Doublet, V.; Labarussias, M.; de Miranda, J.R.; Moritz, R.F.A.; Paxton, R.J. Bees under stress: Sublethal doses of a neonicotinoid pesticide and pathogens interact to elevate honey bee mortality across the life cycle. Environ. Microbiol. 2015, 17, 969–983. [Google Scholar] [CrossRef]
  16. Broadrup, R.L.; Mayack, C.; Schick, S.J.; Eppley, E.J.; White, H.K.; Macherone, A. Honey bee (Apis mellifera) exposomes and dysregulated metabolic pathways associated with Nosema ceranae infection. PLoS ONE 2019, 14, e0213249. [Google Scholar] [CrossRef] [Green Version]
  17. Giannini, T.C.; Costa, W.F.; Borges, R.C.; Miranda, L.; da Costa, C.P.W.; Saraiva, A.M.; Imperatriz Fonseca, V.L. Climate change in the Eastern Amazon: Crop-pollinator and occurrence-restricted bees are potentially more affected. Reg. Environ. Chang. 2020, 20, 9. [Google Scholar] [CrossRef] [Green Version]
  18. Reddy, P.V.R.; Verghese, A.; Rajan, V.V. Potential impact of climate change on honeybees (Apis spp.) and their pollination services. Pest Manag. Hortic. Ecosyst. 2012, 18, 121–127. [Google Scholar]
  19. Piao, S.; Liu, Q.; Chen, A.; Janssens, I.A.; Fu, Y.; Dai, J.; Liu, L.; Lian, X.; Shen, M.; Zhu, X. Plant phenology and global climate change: Current progresses and challenges. Glob. Chang. Biol. 2019, 25, 1922–1940. [Google Scholar] [CrossRef]
  20. Cleland, E.E.; Chuine, I.; Menzel, A.; Mooney, H.A.; Schwartz, M.D. Shifting plant phenology in response to global change. Trends Ecol. Evol. 2007, 22, 357–365. [Google Scholar] [CrossRef]
  21. Vercelli, M.; Novelli, S.; Ferrazzi, P.; Lentini, G.; Ferracini, C. A Qualitative anamlysis of beekeepers’ perceptions and farm anagement adaptations to the impact of climate change on honey bees. Insects 2021, 12, 228. [Google Scholar]
  22. Flores, J.M.; Gil-Lebrero, S.; Gámiz, V.; Rodríguez, M.I.; Ortiz, M.A.; Quiles, F.J. Effect of the climate change on honey bee colonies in a temperate Mediterranean zone assessed through remote hive weight monitoring system in conjunction with exhaustive colonies assessment. Sci. Total. Environ. 2018, 653, 1111–1119. [Google Scholar] [CrossRef]
  23. Goss, M.; Swain, D.L.; Abatzoglou, J.T.; Sarhadi, A.; Kolden, C.A.; Williams, A.P.; Diffenbaugh, N.S. Climate change is increasing the likelihood of extreme autumn wildfire conditions across California. Environ. Res. Lett. 2020, 15, 094016. [Google Scholar] [CrossRef] [Green Version]
  24. Thimmegowda, G.G.; Mullen, S.; Sottilare, K.; Sharma, A.; Mohanta, S.S.; Brockmann, A.; Dhandapany, P.S.; Olsson, S.B. A field-based quantitative analysis of sublethal effects of air pollution on pollinators. Proc. Natl. Acad. Sci. USA 2020, 117, 20653–20661. [Google Scholar] [CrossRef] [PubMed]
  25. Karaman Mayack, B. Modeling disruption of Apis mellifera (honey bee) odorant-binding protein function with high-affinity binders. J. Mol. Recognit. 2023, 36, e3008. [Google Scholar] [CrossRef]
  26. Martinello, M.; Manzinello, C.; Dainese, N.; Giuliato, I.; Gallina, A.; Mutinelli, F. The Honey Bee: An Active Biosampler of Environmental Pollution and a Possible Warning Biomarker for Human Health. Appl. Sci. 2021, 11, 6481. [Google Scholar] [CrossRef]
  27. Bromenshenk, J.J.; Carlson, S.R.; Simpson, J.C.; Thomas, J.M. Pollution Monitoring of Puget Sound with Honey Bees. Science 1985, 227, 632–634. [Google Scholar] [CrossRef]
  28. Simone-Finstrom, M.; Li-Byarlay, H.; Huang, M.H.; Strand, M.K.; Rueppell, O.; Tarpy, D.R. Migratory management and environmental conditions affect lifespan and oxidative stress in honey bees. Sci. Rep. 2016, 6, 32023. [Google Scholar] [CrossRef] [Green Version]
  29. Amiri, E.; Strand, M.K.; Tarpy, D.R.; Rueppell, O. Honey Bee Queens and Virus Infections. Viruses 2020, 12, 322. [Google Scholar] [CrossRef] [Green Version]
  30. Mayack, C.; Naug, D. Parasitic infection leads to decline in hemolymph sugar levels in honeybee foragers. J. Insect Physiol. 2010, 56, 1572–1575. [Google Scholar] [CrossRef]
  31. Mayack, C.; Naug, D. Energetic stress in the honeybee Apis mellifera from Nosema ceranae infection. J. Invertebr. Pathol. 2009, 100, 185–188. [Google Scholar] [CrossRef]
  32. Ramsey, S.D.; Ochoa, R.; Bauchan, G.; Gulbronson, C.; Mowery, J.D.; Cohen, A.; Lim, D.; Joklik, J.; Cicero, J.M.; Ellis, J.D.; et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. USA 2019, 116, 1792–1801. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Wilfert, L.; Long, G.; Leggett, H.C.; Schmid-Hempel, P.; Butlin, R.; Martin, S.J.M.; Boots, M. Deformed wing virus is a recent global epidemic in honeybees driven by Varroa mites. Science 2016, 351, 594–597. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Morammazi, S.; Shokrollahi, B. The pattern of HSP70 gene expression, flight activity and temperature in Apis mellifera meda colonies. J. Therm. Biol. 2020, 91, 102647. [Google Scholar] [CrossRef]
  35. Zaobidna, E.A.; Żółtowska, K.; Łopieńska-Biernat, E. Expression of the Prophenoloxidase Gene and Phenoloxidase Activity, During the Development of Apis mellifera Brood Infected with Varroa Destructor. J. Apic. Sci. 2015, 59, 85–93. [Google Scholar] [CrossRef] [Green Version]
  36. Amdam, G.V.; Norberg, K.; Page, R.E., Jr.; Erber, J.; Scheiner, R. Downregulation of vitellogenin gene activity increases the gustatory responsiveness of honey bee workers (Apis mellifera). Behav. Brain Res. 2006, 169, 201–205. [Google Scholar]
  37. Jackson, J.T.; Tarpy, D.R.; Fahrbach, S.E. Histological Estimates of Ovariole Number in Honey Bee Queens, Apis mellifera, Reveal Lack of Correlation with other Queen Quality Measures. J. Insect Sci. 2011, 11, 1–11. [Google Scholar] [CrossRef] [Green Version]
  38. Szymaś, B.; Łangowska, A.; Kazimierczak-Baryczko, M. Histological structure of the Midgut of honey bees (L.) Fed Pollen Substitutes Fortified with Probiotics. J. Apic. Sci. 2012, 56, 5–12. [Google Scholar]
  39. Koziy, R.V.; Wood, S.C.; Kozii, I.V.; van Rensburg, C.J.; Moshynskyy, I.; Dvylyuk, I.; Simko, E. Deformed Wing Virus Infection in Honey Bees (Apis mellifera L.). Vet. Pathol. 2019, 56, 636–641. [Google Scholar] [CrossRef] [PubMed]
  40. Kozii, I.V.; Wood, S.C.; Koziy, R.V.; Simko, E. Histomorphological description of the reproductive system in mated honey bee queens. J. Apic. Res. 2022, 61, 114–126. [Google Scholar] [CrossRef]
  41. Higes, M.; García-Palencia, P.; Urbieta, A.; Nanetti, A.; Martín-Hernández, R. Nosema apis and Nosema ceranae tissue tropism in worker honey bees (Apis mellifera). Vet. Pathol. 2020, 57, 132–138. [Google Scholar]
  42. Cook, S.; Niño, B.D.; Rivera, L.; Alex, C.E.; Seshadri, A.; Niño, E.L. A practical approach to the collection, fixation, softening, and sectioning of whole honey bees (Apis mellifera) for histologic evaluation. J. Vet. Diagn. 2023, in press. [Google Scholar]
  43. Flottum, K. Better Beekeeping: The Ultimate Guide to Keeping Stronger Colonies and Healthier, More Productive Bees; Quarry Books: Beverly, MA, USA, 2011; p. 176. [Google Scholar]
  44. Caron, D.M.; Connor, L. Honey Bee Biology and Beekeeping; Revised ed.; Wicwas Press: Kalamazoo, MI, USA, 2013; p. 368. [Google Scholar]
  45. Dietemann, V.; Nazzi, F.; Martin, S.J.; Anderson, D.L.; Locke, B.; Delaplane, K.S.; Wauquiez, Q.; Tannahill, C.; Frey, E.; Ziegelmann, B.; et al. Standard methods for varroa research. J. Apic. Res. 2013, 52, 1–54. [Google Scholar] [CrossRef] [Green Version]
  46. Jack, C.J.; Ellis, J.D. Integrated Pest Management Control of Varroa destructor (Acari: Varroidae), the Most Damaging Pest of (Apis mellifera L. (Hymenoptera: Apidae)) Colonies. J. Insect Sci. 2021, 21, 6. [Google Scholar] [CrossRef] [PubMed]
  47. Burgher-MacLellan, K.L.; Williams, G.R.; Shutler, D.; MacKenzie, K.; Rogers, R.E.L. Optimization of duplex real-time PCR with melting-curve analysis for detecting the microsporidian parasites Nosema apis and Nosema ceranae in Apis mellifera. Can. Èntomol. 2010, 142, 271–283. [Google Scholar] [CrossRef]
  48. Mayack, C.; Hakanoğlu, H. Honey Bee Pathogen Prevalence and Interactions within the Marmara Region of Turkey. Veter- Sci. 2022, 9, 573. [Google Scholar] [CrossRef]
  49. Hamiduzzaman, M.M.; Emsen, B.; Hunt, G.J.; Subramanyam, S.; Williams, C.E.; Tsuruda, J.M.; Guzman-Novoa, E. Differential Gene Expression Associated with Honey Bee Grooming Behavior in Response to Varroa Mites. Behav. Genet. 2017, 47, 335–344. [Google Scholar] [CrossRef] [Green Version]
  50. Kuster, R.D.; Boncristiani, H.F.; Rueppell, O. Immunogene and viral transcript dynamics during parasitic Varroa destructor mite infection of developing honey bee (Apis mellifera) pupae. J. Exp. Biol. 2014, 217, 1710–1718. [Google Scholar] [CrossRef] [Green Version]
  51. Zügel, U.; Kaufmann, S.H.E. Role of Heat Shock Proteins in Protection from and Pathogenesis of Infectious Diseases. Clin. Microbiol. Rev. 1999, 12, 19–39. [Google Scholar] [CrossRef] [Green Version]
  52. Arathi, H.S.; Bjostad, L.; Bernklau, E. Metabolomic analysis of pollen from honey bee hives and from canola flowers. Metabolomics 2018, 14, 86. [Google Scholar] [CrossRef]
  53. Arathi, H.S.; Smith, T.J. Drought and temperature stresses impact pollen production and autonomous selfing in a California wildflower, Collinsia heterophylla. Ecol. Evol. 2023, 13, e10324. [Google Scholar]
  54. Emsen, B.; De La Mora, A.; Lacey, B.; Eccles, L.; Kelly, P.G.; Medina-Flores, C.A.; Petukhova, T.; Morfin, N.; Guzman-Novoa, E. Seasonality of Nosema ceranae Infections and Their Relationship with Honey Bee Populations, Food Stores, and Survivorship in a North American Region. Vet. Sci. 2020, 7, 131. [Google Scholar] [CrossRef] [PubMed]
  55. Higes, M.; Meana, A.; Bartolomé, C.; Botías, C.; Martín-Hernández, R. Nosema ceranae (Microsporidia), a controversial 21st century honey bee pathogen. Environ. Microbiol. Rep. 2013, 5, 17–29. [Google Scholar] [CrossRef] [PubMed]
  56. Martín-Hernández, R.; Meana, A.; Prieto, L.; Martínez Salvador, A.; Garrido-Bailón, E.; Higes, M. Outcome of colonization of Apis mellifera by Nosema ceranae. Appl. Environ. Microbiol. 2007, 73, 6331–6338. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Quintero, N.; Viedma, O.; Moreno, J.M. Fire Severity and Drought Conditions Are Increasing in West-Central Spain. Environ. Sci. Proc. 2022, 22, 65. [Google Scholar] [CrossRef]
  58. Smart, M.D.; Sheppard, W.S. Nosema ceranae in age cohorts of the western honey bee (Apis mellifera). J. Invertebr. Pathol. 2012, 109, 148–151. [Google Scholar] [CrossRef]
  59. Seeley, T.D. Division of Labour among Worker Honeybees. Ethology 1986, 71, 249–251. [Google Scholar] [CrossRef]
  60. Natsopoulou, M.E.; McMahon, D.P.; Doublet, V.; Bryden, J.; Paxton, R.J. Interspecific competition in honeybee intracellular gut parasites is asymmetric and favours the spread of an emerging infectious disease. Proc. R. Soc. B Boil. Sci. 2015, 282, 20141896. [Google Scholar] [CrossRef] [PubMed]
  61. Seehuus, S.-C.; Norberg, K.; Gimsa, U.; Krekling, T.; Amdam, G.V. Reproductive protein protects functionally sterile honey bee workers from oxidative stress. Proc. Natl. Acad. Sci. USA 2006, 103, 962–967. [Google Scholar] [CrossRef]
  62. Jacob, D.J.; Winner, D.A. Effect of climate change on air quality. Atmos. Environ. 2009, 43, 51–63. [Google Scholar] [CrossRef] [Green Version]
  63. Millanta, F.; Sagona, S.; Mazzei, M.; Forzan, M.; Poli, A.; Felicioli, A. Phenoloxidase activity and haemolymph cytology in honeybees challenged with a virus suspension (deformed wings virus DWV) or phosphate buffered suspension (PBS). Ciencia Rural 2019, 49. [Google Scholar] [CrossRef] [Green Version]
  64. Gregorc, A.; Evans, J.D.; Scharf, M.; Ellis, J.D. Gene expression in honey bee (Apis mellifera) larvae exposed to pesticides and Varroa mites (Varroa destructor). J. Insect Physiol. 2012, 58, 1042–1049. [Google Scholar] [CrossRef]
  65. Sahebzadeh, N.; Lau, W.H. Expression of heat-shock protein genes in Apis mellifera meda (Hymenoptera: Apidae) after exposure to monoterpenoids and infestation by Varroa destructor mites (Acari: Varroidae). Eur. J. Èntomol. 2017, 114, 195–202. [Google Scholar] [CrossRef] [Green Version]
  66. Gregory, P.G.; Evans, J.D.; Rinderer, T.; de Guzman, L. Conditional immune-gene suppression of honeybees parasitized by Varroa mites. J. Insect Sci. 2005, 5, 7. [Google Scholar] [CrossRef] [PubMed]
  67. Kablau, A.; Berg, S.; Härtel, S.; Scheiner, R. Hyperthermia treatment can kill immature and adult Varroa destructor mites without reducing drone fertility. Apidologie 2020, 51, 307–315. [Google Scholar] [CrossRef]
  68. Porporato, M.; Manino, A.; Cuttini, D.; Lorenzon, S.; Ciaudano, S.; Parodi, V. Varroa Control by Means of a Hyperthermic Device. Appl. Sci. 2022, 12, 8138. [Google Scholar] [CrossRef]
Insects 14 00689 g001
Figure 1. Six study sites and their Fire Hazard Severity Zones as defined by the California Department of Forestry and Fire Protection. (CAL FIRE; https://osfm.fire.ca.gov/divisions/community-wildfire-preparedness-and-mitigation/wildfire-preparedness/fire-hazard-severity-zones/, accessed on 4 April 2023). Fire hazard zones: Insects 14 00689 i001 Very high Insects 14 00689 i002 High Insects 14 00689 i003 Moderate.
Figure 1. Six study sites and their Fire Hazard Severity Zones as defined by the California Department of Forestry and Fire Protection. (CAL FIRE; https://osfm.fire.ca.gov/divisions/community-wildfire-preparedness-and-mitigation/wildfire-preparedness/fire-hazard-severity-zones/, accessed on 4 April 2023). Fire hazard zones: Insects 14 00689 i001 Very high Insects 14 00689 i002 High Insects 14 00689 i003 Moderate.
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Insects 14 00689 g002
Figure 2. The correlation between air quality, (A) Prophenoloxidase (ProPO) normalized gene expression (ρ = −0.2839, df = 63, p = 0.023), (B) Vitellogenin (Vg) normalized gene expression (ρ = 0.26, df = 63, p = 0.038), and (C) Maximum daily temperature (ρ = 0.28, df = 63, p = 0.027).
Figure 2. The correlation between air quality, (A) Prophenoloxidase (ProPO) normalized gene expression (ρ = −0.2839, df = 63, p = 0.023), (B) Vitellogenin (Vg) normalized gene expression (ρ = 0.26, df = 63, p = 0.038), and (C) Maximum daily temperature (ρ = 0.28, df = 63, p = 0.027).
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Figure 3. The correlation plots between (A) N. ceranae load in forager bees and colony Varroa mite loads (ρ = 0.51, df = 60, p < 0.0001), (B) N. ceranae loads in in-hive bees and colony Varroa mite loads (ρ = 0.43, df = 63, p = 0.004), (C) Normalized Prophenoloxidase (ProPO) gene expression and N. ceranae loads in forager bees (ρ = 0.27, df = 63, p = 0.03), (D) Normalized Prophenoloxidase (ProPO) gene expression in forager bees and colony Varroa mite loads (ρ = 0.0037, df = 60, p = 0.98), (E) Heat Shock Protein 70 (HSP70) gene expression in forager bees and colony Varroa mite loads (ρ = −0.12, df = 60, p = 0.34) and (F) Prophenoloxidase (ProPO) gene expression in in-hive bees and Heat Shock Protein 70 (HSP70) gene expression in in-hive bees (ρ = 0.60, df = 65, p < 0.0001).
Figure 3. The correlation plots between (A) N. ceranae load in forager bees and colony Varroa mite loads (ρ = 0.51, df = 60, p < 0.0001), (B) N. ceranae loads in in-hive bees and colony Varroa mite loads (ρ = 0.43, df = 63, p = 0.004), (C) Normalized Prophenoloxidase (ProPO) gene expression and N. ceranae loads in forager bees (ρ = 0.27, df = 63, p = 0.03), (D) Normalized Prophenoloxidase (ProPO) gene expression in forager bees and colony Varroa mite loads (ρ = 0.0037, df = 60, p = 0.98), (E) Heat Shock Protein 70 (HSP70) gene expression in forager bees and colony Varroa mite loads (ρ = −0.12, df = 60, p = 0.34) and (F) Prophenoloxidase (ProPO) gene expression in in-hive bees and Heat Shock Protein 70 (HSP70) gene expression in in-hive bees (ρ = 0.60, df = 65, p < 0.0001).
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Figure 4. A box plot representing the N. ceranae load per colony found across (A) time and (B) Apiaries for both forager and in-hive bees. Medians are represented by solid lines within each box plot, and the boxes represent the inter-quartile range. The grey dashed line across the figure represents the threshold (1 million spores) at which a beekeeper is advised to treat their hives. The letters denote significant differences across months at the alpha = 0.05 level. The numbers above the bars represent sample sizes. (C) A dot plot representing the N. ceranae load in foragers and in-hive bees was pooled across all sampling sites and time points. The black horizontal line indicates which groups are being compared with the statistical test. The ** indicates a significant difference at p < 0.01.
Figure 4. A box plot representing the N. ceranae load per colony found across (A) time and (B) Apiaries for both forager and in-hive bees. Medians are represented by solid lines within each box plot, and the boxes represent the inter-quartile range. The grey dashed line across the figure represents the threshold (1 million spores) at which a beekeeper is advised to treat their hives. The letters denote significant differences across months at the alpha = 0.05 level. The numbers above the bars represent sample sizes. (C) A dot plot representing the N. ceranae load in foragers and in-hive bees was pooled across all sampling sites and time points. The black horizontal line indicates which groups are being compared with the statistical test. The ** indicates a significant difference at p < 0.01.
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Figure 5. Box plots representing the number of Varroa mites per colony found across (A) Time and (B) Apiary sampled from the colony. Medians are represented by the solid lines within each box plot, and the boxes represent the inter-quartile range. The black dashed line across the figure represents the threshold (9 per 300 bees) at which a beekeeper is advised to treat their hive. The letters denote significant differences across the locations at the alpha = 0.05 level. The numbers above the letters represent the sample size.
Figure 5. Box plots representing the number of Varroa mites per colony found across (A) Time and (B) Apiary sampled from the colony. Medians are represented by the solid lines within each box plot, and the boxes represent the inter-quartile range. The black dashed line across the figure represents the threshold (9 per 300 bees) at which a beekeeper is advised to treat their hive. The letters denote significant differences across the locations at the alpha = 0.05 level. The numbers above the letters represent the sample size.
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Figure 6. Representative histologic sections stained with Hematoxylin and eosin (H&E) stain through the head the for total area of hypopharyngeal glands (A) and mandibular glands (B). Image analysis. The highlighted areas indicate the total area measured. Scale bars represent 100 µM.
Figure 6. Representative histologic sections stained with Hematoxylin and eosin (H&E) stain through the head the for total area of hypopharyngeal glands (A) and mandibular glands (B). Image analysis. The highlighted areas indicate the total area measured. Scale bars represent 100 µM.
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Figure 7. Percentage of Nosema spp. infection based on histologic evaluation over June–November 2021. The number 1–6 refer to the six different locations in the Napa, Yolo, and Solano counties of central California shown in Figure 1. Percent infection is based on ten nurse bees (black bars) and ten forager bees (gray bars) for each site and at each time point.
Figure 7. Percentage of Nosema spp. infection based on histologic evaluation over June–November 2021. The number 1–6 refer to the six different locations in the Napa, Yolo, and Solano counties of central California shown in Figure 1. Percent infection is based on ten nurse bees (black bars) and ten forager bees (gray bars) for each site and at each time point.
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Figure 8. Representative microscopic images of Nosema spp. and sections of ventriculus in which epithelial cells were distended with abundant spores (arrow) and ventricular luminal spores (arrowhead), stained with Hematoxylin and eosin (H&E) stain. The scale bar represents 50 µM, and the inset scale bar represents 10 µM.
Figure 8. Representative microscopic images of Nosema spp. and sections of ventriculus in which epithelial cells were distended with abundant spores (arrow) and ventricular luminal spores (arrowhead), stained with Hematoxylin and eosin (H&E) stain. The scale bar represents 50 µM, and the inset scale bar represents 10 µM.
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MDPI and ACS Style

Mayack, C.; Cook, S.E.; Niño, B.D.; Rivera, L.; Niño, E.L.; Seshadri, A. Poor Air Quality Is Linked to Stress in Honeybees and Can Be Compounded by the Presence of Disease. Insects 2023, 14, 689. https://doi.org/10.3390/insects14080689

AMA Style

Mayack C, Cook SE, Niño BD, Rivera L, Niño EL, Seshadri A. Poor Air Quality Is Linked to Stress in Honeybees and Can Be Compounded by the Presence of Disease. Insects. 2023; 14(8):689. https://doi.org/10.3390/insects14080689

Chicago/Turabian Style

Mayack, Christopher, Sarah E. Cook, Bernardo D. Niño, Laura Rivera, Elina L. Niño, and Arathi Seshadri. 2023. "Poor Air Quality Is Linked to Stress in Honeybees and Can Be Compounded by the Presence of Disease" Insects 14, no. 8: 689. https://doi.org/10.3390/insects14080689

APA Style

Mayack, C., Cook, S. E., Niño, B. D., Rivera, L., Niño, E. L., & Seshadri, A. (2023). Poor Air Quality Is Linked to Stress in Honeybees and Can Be Compounded by the Presence of Disease. Insects, 14(8), 689. https://doi.org/10.3390/insects14080689

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