ABSTRACT
Insect pollination sustains the biodiversity of 90% of wild plants, and 75% of crop species for food and nutritional security. Chemical pesticides used to manage arthropod pests constitute a key driver to the unprecedented declines of insect pollinators worldwide. Hence, biopesticides based on entomopathogenic fungi (EPF) are being promoted as safer alternatives. The effects of EPF on insect pollinators have not been investigated in detail for the application in pollinator-resourced crop systems. Thus, this study screened EPF isolates of Metarhizium anisopliae (ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69 and ICIPE 78), and Beauveria bassiana (ICIPE 284) for their effect on the Western honey bee (Apis mellifera) and African stingless bee (Meliponula ferruginea). The study was undertaken at the international centre of Insect Physiology and Ecology (icipe), Nairobi, Kenya, from November 2019 through February 2021. In the first part of the study, groups of 25–30 bees/cage were exposed to surfaces sprayed separately with six isolates (108 conidial/mL) or sterile water (control) and incubated for 10 days. The exposure assay was replicated four times and repeated twice for each bee species, and conidial acquisition was evaluated on five bees/cage. Apis mellifera acquired more conidia (2.8 × 104–1.3 × 105 colony-forming units [CFU]/bee) than M. ferruginea (1.1 × 104–2.3 × 104 CFU/bee) based on the analysis of variance. Except for ICIPE 7, ICIPE 20 and ICIPE 69 which caused significant A. mellifera mortality (25.8–40.4%) in the first experiment, none of the isolates had a significant effect on either of the bee species according to survival analysis. The isolates are harmless and/or slightly harmful to bees according to the International Organization of Biological Control classification. Bee colonies inherently thermoregulate their hives and, thus, the second part of the study evaluated the performance of six isolates in bee colonies using eight predictive models describing thermal requirements; (minimum [Tmin], optimal [Topt] and maximum [Tmax] thresholds; and maximal performance [Pmax]). The isolates were incubated at 12, 16, 20, 24, 28, 32 and 36°C, and conidial germination and mycelial growth were measured and fitted to the models. Models were compared numerically (the Akaike information criterion [AIC], adjusted R2) and statistically (likelihood ratio test). The best models were the cardinal temperature model with inflection (CTMI) and Ratkowsky 3 for germination; and CTMI, Ratkowsky 2 and Lactin 1 for growth. Temperature nonlinearly affected the isolates’ performance and the isolates had different thermal requirements. Germination had Tmin, Topt, Tmax and Pmax of 13.2–14.2°C, 26.2–28.9°C, 35.7–36.3°C and 95.4–100.0%; while growth had 7.0–13.2°C, 25.9–28.4°C, 34.5–37.9°C and 1.36–2.28 mm/day, respectively. The low Topt indicate that the isolates are unlikely to operate in bee colonies. Best-fitting models can be routinely used in the selection and re-evaluation of EPF candidates. The third part of the study involved the application of M. anisopliae ICIPE 69 in two greenhouses. Greenhouses were partitioned into plots and planted with cucumber (Cucumis sativus) following good agricultural practices. Each plot was installed with a colony of M. ferruginea at blooming inception and the crops were sprayed with either ICIPE 69 or sterile water (control). The trials were repeated three times in a completely randomized block design. Colony survival, pollination behaviour, fruit set and yield, and persistence on crops were recorded within 9 days before until 18 days after treatment application. Collected data were analysed using generalized linear models. ICIPE 69 isolate did not result in a significant effect on these parameters while conidial acquisition by foragers and persistence on crops declined periodically. These tiered studies establish that EPF developed in Africa can be safely used in integrated pest and pollinator management (IPPM) programmes.
Keywords: Apis mellifera, Entomopathogenic fungi, Nonlinear model, Survivorship.
TABLE OF CONTENTS
Declaration ii
Dedication iii
Acknowledgement iv
List of figures viii
List of tables ix
Abbreviations and acronyms x
Abstract xii
Chapter One
1.0. General introduction 14
1.1. Background to the study 14
1.2. Statement of the problem and justification of the study 16
1.3. Objectives 18
1.3.1. Broad objective 18
1.3.2. Specific objectives 18
1.4. Research hypothesis 19
Chapter Two
2.0. Literature review 20
2.1. Pollinators and their ecosystem services 20
2.2. Pollination crisis and drivers to the decline of pollinators 21
2.3. Effect of chemical pesticides on pollinators 22
2.4. Entomopathogenic fungi 23
2.4.1. Overview of entomopathogenic fungi 23
2.4.2. Host selection and mode of action of entomopathogenic fungi 24
2.4.3. Entomopathogenic fungi as biological control agents of pests 26
2.5. Application, interaction, and safety of entomopathogenic fungi on bees 27
2.5.1. Management of crop pests and sensitivity of bees as pollinators 27
2.5.2. Management of bees’ pathogens and safety to bees 28
2.5.3. Entomovectoring technology and safety of bees as entomovectors 30
2.6. Response of bees to entomopathogenic fungi 32
2.6.1. Immune response against mycotic invasion 32
2.6.2. Behavioural responses and protection against fungal invasion 32
2.6.3. Role of bee thermoregulation on mycotic invasion 33
Chapter Three
3.0. Safety of Metarhizium anisopliae Metsch. and Beauveria bassiana Bals. To the honey bee (Apis mellifera L.) and the stingless bee (Meliponula ferruginea Cockrell) (Hymenoptera: Apidae) in laboratory conditions 35
Abstract 35
3.1. Introduction 36
3.2. Materials and methods 38
3.2.1. Source and culturing of fungal isolates 38
3.2.2. Assessment of conidial germination 40
3.2.3. Preparation of fungal suspensions 41
3.2.4. Source and in vitro maintenance of Apis mellifera 41
3.2.5. Source and in vitro maintenance of Meliponula ferruginea 43
3.2.6. Exposure of caged bees to entomopathogenic fungal isolates 44
3.2.7. Assessment of conidial acquisition by the bees 44
3.2.8. Assessment of the effect of entomopathogenic fungi on bees 45
3.2.9. Data analysis 46
3.3. Results 48
3.3.1. Conidial acquisition by bees 48
3.3.2. Post-exposure survival of bees 48
3.3.3. Time-response mortality of fungus-exposed bees 52
3.3.4. Mycosis of fungus-exposed bees 53
3.3.5. Correlation of conidial acquisition with pathogenicity of fungi 54
3.4. Discussion 56
Chapter Four
4.0. Modelling growth performance of Metarhizium anisopliae and Beauveria bassiana isolates under bee colonies’ temperatures 60
Abstract 60
4.1. Introduction 61
4.2. Materials and methods 63
4.2.1. Determination of the effect of temperature on conidial germination 63
4.2.2. Determination of the effect of temperature on mycelial growth 64
4.2.3. Nonlinear models for the study 65
4.2.4. Data analysis 67
4.3. Results 68
4.3.1. Comparison of the models for conidial germination 68
4.3.2. Comparison of the models for mycelial growth 71
4.3.3. Cardinal estimates for conidial germination and mycelial growth 74
4.4. Discussion 77
Chapter Five
5.0. Effect of entomopathogenic fungus (Metarhizium anisopliae) on survival, pollination behaviour and pollination success of African stingless bee Meliponula ferruginea pollinating cucumber Cucumis sativus 81
Abstract 81
5.1. Introduction 82
5.2. Material and methods 83
5.2.1. Preparation of Metarhizium anisopliae icipe 69 83
5.2.2. Meliponula ferruginea colony 84
5.2.3. Experimental setup 84
5.2.4. The application of Metarhizium anisopliae icipe 69 87
5.2.5. Determination of flight intensity, foraging activity, and survival of bees 87
5.2.6. Determination of fruit set, development, and yield 88
5.2.7. Determination of conidial acquisition and pollen load by bees 89
5.2.8. Evaluation of persistence and viability of conidia on cucumber crop 90
5.2.9. Data analysis 90
5.3. Results 91
5.3.1. Flight activity, foraging activity, and survival of forager bees 91
5.3.2. Fruit set, development, and yield 95
5.3.3. Conidial acquisition and persistence 97
5.4. Discussion 98
Chapter Six
6.0. General conclusions and recommendations 102
6.1. General conclusions 102
6.2. Recommendations 104
References 105
LIST OF FIGURES
Figure
3.1 : Micro-spray tower for applying entomopathogenic fungi on filter paper during exposure bioassay and incubators for maintaining exposed bees. 46
3.2 : Kaplan–Meier survival curves for Apis mellifera exposed to Metarhizium anisopliae and Beauveria bassiana 50
3.3 : Kaplan–Meier survival curves of Meliponula ferruginea exposed to Metarhizium anisopliae and Beauveria bassiana 51
3.4 : Mycosis of caged Apis mellifera (a) and Meliponula ferruginea (b) exposed to
Metarhizium anisopliae and Beauveria bassiana 54
3.5 : Scatter plots showing linear relationships between conidial acquisition of Metarhizium anisopliae, and Beauveria bassiana isolates with LT10 (a) and mycosis (b) for Apis mellifera 55
3.6 : Scatter plots showing linear relationships between conidial acquisition of Metarhizium anisopliae, and Beauveria bassiana isolates with LT10 (a) and mycosis (b) for Meliponula ferruginea 56
4.1 : Representation of (a) conidial viability assessment by enumerating percentage germination of conidia and (b) measuring of daily radial growth of the fungus along two cardinal lines intersecting perpendicularly at the bottom centre of Petri dishes. 64
4.2 : Curves of nonlinear models predicting the effect of temperature on conidial germination of Metarhizium anisopliae and Beauveria bassiana. CTMI = cardinal temperature model with inflection. 69
4.3 : Curves of nonlinear models predicting the effect of temperature on mycelial growth of Metarhizium anisopliae (ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, and ICIPE 78) and Beauveria bassiana (ICIPE 284). CTMI = cardinal temperature model with inflection. 72
5.1 : Experimental layout in greenhouses and one greenhouse compartment containing young cucumber Cucumis sativus plants. 85
5.2 : Representation of treatment plot containing blooming cucumber Cucumis sativus
plants and installed with colony of Meliponula ferruginea 86
5.3 : Stingless bee Meliponula ferruginea foraging on a male flower (a) and a female flower (b) of cucumber Cucumis sativus 88
5.4 : Physiological mature cucumber Cucumis sativus fruit (14–day–old fruit, tagged with red thread) ready for harvest (a) and measurement in the laboratory (b). 89
5.5 : Flight activity of Meliponula ferruginea in treatment plots containing flowering
Cucumis sativus 93
5.6 : Foraging activity of Meliponula ferruginea in treatment plots containing flowering Cucumis sativus 94
5.7 : Meliponula ferruginea survival from plots containing treated Cucumis sativus.95
5.8 : Conidial viability on Cucumis sativus after biopesticide spray. 98
LIST OF TABLES
Table
2.1: Potential of pollinators as entomovectors of fungal-based biological control agents (BCAs) in pest management and associated side effects. 31
3.1 : Fungal isolates, their origins, target pests and commercialization. 39
3.2 : Colony-forming units per bee after exposure to Metarhizium anisopliae ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, ICIPE 78 and Beauveria bassiana ICIPE 284.48
3.3 : Lethal times (LT) and 95% fiducial limits (FL) for Apis mellifera and Meliponula ferruginea exposed to Metarhizium anisopliae and Beauveria bassiana. 52
4.1 : Comparison of nonlinear models used to predict the effect of temperature on conidial germination of Metarhizium anisopliae ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, ICIPE 78, and Beauveria bassiana ICIPE 284. 70
4.2 : Likelihood ratio test (z values) between the best-fitting model and other models for conidial germination of Metarhizium anisopliae (ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, ICIPE 78) and Beauveria bassiana (ICIPE 284) isolates. 70
4.3 : Comparison of nonlinear models used to predict the effect of temperature on mycelial growth of Metarhizium anisopliae ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, ICIPE 78, and Beauveria bassiana ICIPE 284. 73
4.4 : Comparison between the best-fitting model and other models for mycelial growth of Metarhizium anisopliae (ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, ICIPE 78) and Beauveria bassiana (ICIPE 284) isolates. 73
4.5 : Models’ estimates for conidial germination of Metarhizium anisopliae ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, ICIPE 78 and Beauveria bassiana ICIPE 284.75
4.6 : Models’ estimates for mycelial growth for Metarhizium anisopliae ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, ICIPE 78 and Beauveria bassiana ICIPE 284.76
5.1 : Fruit set and maturation of Cucumis sativus pollinated by Meliponula ferruginea
9 days before until 9 days after treatment application. 96
5.2 : Fruit weight (g) of Cucumis sativus pollinated by Meliponula ferruginea at 9 days before until 9 days after treatment application. 97
5.3 : Biopesticide Colony-forming unitsy (CFU) retained on the Cucumis sativus plant, and CFU and pollen collected by Meliponula ferruginea foragers. 97
ABBREVIATIONS AND ACRONYMS
Adj. R2 Adjusted R–squared
AIC Akaike information criterion
ANOVA Analysis of variance
API Africa Pollinator Initiative
APU Arthropod Pathology Unit
AU–IBAR African Union–InterAfrican Bureau for Animal Resources
BCA Biological control agents
BZM Federal Ministry for Economic Corporation and Development
BOD Biological oxygen demand
CCD Colony Collapse Disorder
chi Chitinase gene
CFU Colony-forming units
CTMI Cardinal temperature model
DFID U.K.’s Department for International Development
DRIP Dissertation Research Internship Programme.
EPF Entomopathogenic fungi
Eqn Equation
FL Fiducial limit
GAP Good agricultural practices
GDP Gross domestic product
icipe International Centre of Insect Physiology and Ecology
IOBC International Organization of Biological Control
IUCN International Union for Conservation of Nature
IPBES Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services
IPM Integrated pest management
IPPM Integrated pest and pollinator management
LT Lethal time-response
PDA Potato dextrose agar
OATH Original Australian Trigona Hives
Pmax Maximum germination or growth rate
PSI Pounds per square inch
RH Relative humidity
rpm Revolution per minute
SDC Swiss Agency for Development and Cooperation
Sida Swedish International Development Cooperation Agency
SDA Sabouraud dextrose agar
Tmax Maximum temperature
Tmin Minimum temperature
Topt Maximum temperature
CHAPTER ONE
1.0. GENERAL INTRODUCTION
1.1. Background to the study
Insect pollination is an essential ecosystem service underpinning 90 and 75% of flowering wild plant and crop species, respectively (IPBES, 2016). This service is universally associated with improving crop yield and quality, a prerequisite to resilient food and nutrition security (Bartomeus et al., 2014; Garibaldi et al., 2013). The global contribution of insect pollination services in commercial crops is currently estimated to be between $267–657 billion USD annually (Porto et al., 2020). Moreover, apiculture and meliponiculture are the upcoming practices especially in Africa (AU– IBAR, 2019). These practices are associated with several hive products such as honey, wax, propolis, bee venom and royal jelly which are increasingly used in several industries including food and medicine industries, forming sources of livelihood to many farmers and stakeholders (Pasupuleti et al., 2017). However, there are threats to the global food basket. Notably, most high-commodity and pollinator-dependent crops are under constant attack by arthropod pests. In the context of Africa, the productivity of key crops are decimated by several arthropod pests (Kambura et al., 2018; Odanga et al., 2017; Sharma et al., 2016; Badii et al., 2015). As a result, damages and phytosanitary restrictions caused by these pests have prompted heavy applications of chemical pesticides (Badii et al., 2015).
Consistent applications of broad-spectrum chemical pesticides have negatively affected nontarget insects (Mullin et al., 2010; Desneux et al., 2007). Markedly, chemical pesticides coupled with environmental perturbations and pathogens constitute key stressors to the increasing global declines of pollinators (IPBES, 2016; Garibaldi et al., 2010; Potts et al., 2010). Consequently, low crop productivity and dwindling hive product outputs have been documented and are increasingly becoming a global concern threatening food security (IPBES, 2016; Vanbergen et al., 2013) and livelihoods of farmers in the crop farming and beekeeping sectors (AU–IBAR, 2019).
Biological control approach based on entomopathogenic fungi (EPF) is considered a better alternative to chemical pest control (De Faria and Wraight, 2007). EPF are increasingly adopted for their eco-friendliness, bio-specificity, and ease of mass production (Maina et al., 2018). In Africa, entomopathogenic fungi (EPF), mainly isolates of Metarhizium anisopliae (Metsch.) Sorokin, have been developed into biological control products especially during the last two decades (Akutse et al., 2020). Their efficacy has been widely demonstrated on several agricultural pests (Niassy et al., 2012; Ekesi et al., 2007).
During pest mitigation practices in pollinator-based crop systems, EPF applied on crops may affect the survival of forager bees or affect their foraging behaviour including flight activity, flower visitation rate, pollen collection and consequently affecting fruit set and yield of the crops. EPF introduced intentionally or unintentionally into beehives can remain viable, infect the bees and/or contaminate hive products. However, eusocial bees can avoid the effect of EPF through sophisticated hygienic behaviours and inherent thermoregulation of internal hive temperatures to an average range of 31.0–36.0ºC (Jarimi et al., 2020). At this temperature range, growth of many EPF is reportedly restricted, however, some can still grow maximally to cause infections (Davidson et al., 2003). To expediently describe the temperature-growth interactions of EPF by simulating hive temperatures, suitable predictive models need to be adopted.
Nonlinear models are essential tools widely used in food microbiology to predict the effect of static and dynamic biophysical conditions including temperatures on the growth of food spoilage and toxigenic bacteria (Huang et al., 2011; Rosso et al., 1995; Zwietering et al., 1991) and fungi (Peleg and Normand, 2013; Gougouli and Koutsoumanis, 2013, 2012; Dantigny et al., 2011). Though, comparatively few temperature-dependent models have been tested on EPF (Davidson et al., 2003; Smits et al., 2003; Fargues et al., 1997).
Information on the effect of EPF on bees and their ability to thrive in bee nests are critical to warrant their usage in integrated pest management (IPM) and integrated pest and pollinator management (IPPM) programmes. Thus, the present study evaluated the potential effect of six commercialized EPF isolates of M. anisopliae and Beauveria bassiana (Bals.) Vuill. on the Western honey bee (Apis mellifera L.) (Hymenoptera: Apidae) and the African stingless bee (Meliponula ferruginea Cockrell) (Hymenoptera: Apidae) under laboratory and semi-field conditions. These EPF isolates have been registered as Campaign® (Metarhizium anisopliae ICIPE 69), Achieve® (Metarhizium anisopliae ICIPE 78), Supreme® (Metarhizium anisopliae ICIPE 62) and TickOff® (Metarhizium anisopliae ICIPE 7) while Metarhizium anisopliae ICIPE 20 and Beauveria bassiana ICIPE 284 are in pipeline for commercial use (Akutse et al., 2020). The effect of bee colonies’ conditions on viability and growth of these isolates was assessed using predictive models.
1.2. Statement of the problem and justification of the study
Agriculture is the economic mainstay of many African countries which provides full– time employment to 70% of the population, accounting for one-third of the gross domestic product (GDP) and 40% foreign exchange earnings (AU–IBAR, 2019). In this sector, pollinators and pests remain insects of economic importance. Pollinators, specifically bees, provide pollination services, hive products and by-products. Therefore, bees need to be conserved to sustainably provide these ecosystem services. On the other hand, arthropod pests decimate crop productivity and pesticides have been broadly used as mitigation measures (Warra and Prasad, 2020), but their application has negatively affected beneficial insects primarily bees (Böhme et al., 2018).
EPF as biological control agents (BCAs) are being championed because most of them are arguably harmless to nontarget and beneficial organisms (Zimmermann, 2007) and their residues are unlikely to be traced in agricultural products (Maina et al., 2018). Additionally, they are self-perpetuating in the habitats of the pests to provide extended protection and are unlikely to trigger a resistant population of pests compared to chemical insecticides (Kidanu and Hagos, 2020).
However, the ecological risks of currently developed EPF on principal pollinators especially bees remain least explored in detail. Previous laboratory studies have shown variable effects of EPF on bees depending on exposed species of bees (Toledo– Hernandez et al., 2016), the species and isolates of EPF (Espinosa–Ortiz et al., 2011), and the tested concentrations and exposure methods (Potrich et al., 2018). This indicates that candidate EPF may or may not be safe for bees. To explicitly understand the effect of EPF, tiered studies are essentially required. Predictive models, used in forecasting the effect of temperature ranges on growth of several microbes (Peleg and Normand, 2013; Gougouli and Koutsoumanis, 2010) may be important tools in predicting the effect of temperature on EPF in bee colonies. The use of predictive models in describing the growth performance of EPF in conditions of the bee as pollinators and target pests may be critical in designing IPPM programmes. During the management of pests, EPF applied as biopesticides on flowering crops may impair foraging activities and survival of pollinators and consequently, the reduction of crop yield. Therefore, tiered studies on the nontarget effects of EPF will help in the selection of EPF candidates for the application in pollinator-resourced crop systems.
1.3. Objectives
1.3.1. Broad objective
This study aimed at assessing the nontarget effect of five isolates of M. anisopliae (ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, ICIPE 78) and one isolate of B. bassiana (ICIPE 284) on A. mellifera and M. ferruginea under laboratory and semi-field conditions, and to predict the germination and growth of these isolates under beehives’ temperatures.
1.3.2. Specific objectives
i. To assess the level of conidial acquisition and safety of M. anisopliae and B. bassiana to A. mellifera and M. ferruginea under laboratory conditions.
ii. To establish nonlinear models to describe the effect of bee colonies’ simulated temperatures on conidial germination and mycelial growth of M. anisopliae and B. bassiana.
iii. To investigate the effect of M. anisopliae on survival, pollination behaviour and pollination efficiency of M. ferruginea and establish their persistence on cucumber Cucumis sativus L. under greenhouse conditions.
1.4. Research hypothesis
i. Apis mellifera and M. ferruginea can acquire conidia of biopesticides M. anisopliae (ICIPE 7, ICIPE 20, ICIPE 62, ICIPE 69, ICIPE 78) and B. bassiana (ICIPE 284) but with not any negative affect their survival.
ii. There are predictive models to suitably describe conidial germination and mycelial growth of M. anisopliae and B. bassiana in hive-simulate temperature
iii. Spraying crops with biopesticides can be retained on crop surfaces but cannot affect M. ferruginea survival, pollination behaviour and crop yield.
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