research on biological control of pest

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Biocontrol strategies: an eco-smart tool for integrated pest and diseases management

• Durgesh Kumar Jaiswal   ORCID: orcid.org/0000-0003-0198-3726 1 ,
• Suresh Janardhan Gawande 2 ,
• P. S. Soumia 2 ,
• Ram Krishna 3 ,
• Anukool Vaishnav 4 &
• Avinash Bapurao Ade 1  

BMC Microbiology volume  22 , Article number:  324 ( 2022 ) Cite this article

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For the burgeoning global population, sustainable agriculture practices are crucial for accomplishing the zero-hunger goal. The agriculture sector is very concerned about the rise in insecticide resistance and the Modern Environmental Health Hazards (MEHHs) that are problems for public health due to on pesticide exposure and residues. Currently, farming practices are being developed based on microbial bio-stimulants, which have fewer negative effects and are more efficient than synthetic agro-chemicals. In this context, one of the most important approaches in sustainable agriculture is the use of biocontrol microbes that can suppress phytopathogens and insects. Simultaneously, it is critical to comprehend the role of these microbes in promoting growth and disease control, and their application as biofertilizers and biopesticides, the success of which in the field is currently inconsistent. Therefore, editorial is part of a special issue titled "Biocontrol Strategies: An Eco-smart Tool for Integrated Pest and Disease Management" which focuses on biocontrol approaches that can suppress the biotic stresses, alter plant defense mechanisms, and offer new eco-smart ways for controlling plant pathogens and insect pests under sustainable agriculture.

Backgrounds

By 2050, there will be 10 billion people on the planet, and feeding them is the biggest challenge facing global agriculture [ 1 , 2 ]. Plants are the only direct source that can supply humans with 90% of their calories and 80% of their protein. To meet rising global demand, food production is currently being increased in earnest on a worldwide scale [ 3 , 4 ]. Biotic and abiotic stressors are the main barrier to sustainable food production. These issues have recently grown to be of great concern on a global scale [ 5 , 6 ]. The yearly economic loss from biotic stressors is $40 billion and results in crop losses of 20–40% [ 4 , 7 , 8 ]. Many serious social issues were reported due to the infestation of pathogens in food crops like  Phytophthora infestans  pathogen, responsible for potato late blight, which wiped out a million Irish people and forced another 1.5 million to leave their homes in the 1840s; it left an indelible mark on human history [ 9 ]. Another classical case of late blight causes an annual loss of $6.7 billion to the potato industry.

Similarly, due to the rice brown leaf spot disease caused by  Helminthosporium oryzae . Many serious social issues were reported due to the infestation of pathogens in food crops like Phytophthora infestans, which caused potato late blight wiped out almost a million Irish people and forced another 1.5 million to flee their homes in the 1840s, and left an indelible mark on human history [ 9 ]. According to conservative estimates, the potato sector suffers a yearly loss of $6.7 billion due to the late blight, as does the rice industry from brown leaf spots caused  by H. oryzae . Two million people were estimated to die during the 1940s due to the devastating famine in Bengal, which negatively influenced rice output. The corn leaf blight pandemic caused by  Helminthosporium maydis  devastated 15% of the maize harvest in the United States and cost an estimated $1 billion in 1970 [ 10 , 11 ]. The catastrophic effects of pandemic plant pests have affected all the continents of the world. Therefore, efficient and eco-friendly disease management tools are pre-requisite for the global food, fiber, and biomaterials supply chains [ 12 ].

Microbiologists, plant pathologists, and entomologists across the globe face a significant challenge as they work to find and develop environmentally friendly control agents against plant diseases & pests. Their goal is to reduce the widespread use of chemical pesticides, which would be an important step forward. On the other hand, pesticides and biopesticides derived from beneficial microorganisms are among the most effective strategies for risk-free crop management during low to medium biotic stresses. Numerous early publications [ 13 , 14 , 15 , 16 , 17 , 18 ] and reviews [ 19 , 20 , 21 , 22 , 23 ] on this issue have been published, reflecting the continually expanding interest in this field of study [ 24 , 25 , 26 ]. Additionally, due to the alarming rise in recent pathogen alerts and concerns about food security, all major agribusiness corporations are now investing in developing biological applications [ 27 , 28 , 29 ]. Researchers have concluded that biological control will remain indispensable and play a significant role in modern agriculture. A decline in biocontrol adoption around 2000 years has given way to significantly increased adoption in the last five years, largely due to supplementary biological control [ 30 , 31 ], for which the political changes in Latin America, Asia, and Europe are to blame. Due to this, from 2017 to 2021, the amount of crop protection chemicals used globally dropped from 2.75 to 2.66 million metric tones ( https://www.statista.com/statistics/1263077/global-pesticide-agricultural-use/ ).

Increased consumer demands due to the awareness created by researchers, academicians, and non-governmental organizations have all been hastening this shift. Growing educational opportunities in plant protection training over the past few decades have led to the widespread successful use of biological control, particularly in developing countries like Brazil, where research and implementation of both augmentative and classical biocontrol are gaining momentum. This trend has been accelerated by the demands due to the awareness created by researchers, academicians, and non-governmental organizations that have all been hastening this shift. Growing education opportunities in plant protection training over the past few decades have resulted in the successful use of biological control on a large scale, especially in developing nations like India, China & Brazil, where research and implementation of both augmentative and classical biocontrol are gaining momentum. Realizing that synthetic pesticides and fertilizers have damaged ecosystems and exacerbated food security concerns, China and India have invested in biological control research, training, and adoption [ 31 , 32 , 33 ].

The growing concerns about the overuse of synthetic chemical pesticides and their residues, increased significance of insect pests and pathogens due to increased food demand, the withdrawal of several chemical pesticides, including soil fumigants, the appearance of new invasive species, and pesticide-resistant strains of pests, climate change, and specialized monoculture are all factors have contributed to the expansion of the biological control domain of plant protection under the sustainable agriculture goal. However, bio-control agents (BCAs) have advantages over traditional crop protection (CPs) methods but are not yet ready to take their place. In many cases, the adaptability of BCA in a non-native environment is poor. Further, their efficacy against multiple pathogens/insect pests is also low. As a result, it hasn't been widely used [ 34 ].

Another major challenge is the lack of adequate characterization of bio-agents coupled with the poor marketing strategy of bioagent-producing firms; for example, many PGPRs/biofertilizers projected as a biocontrol agent; low efficacy in non-native soils/environment work on adaptability, and its contains is lacking appropriate research on the efficacy, growth promoting activities; lacking response of bio-agents at the physiological and molecular level, and poor of characterization and product formulation of bio-agents. The mild disease/pest suppression by these bio-agents may probably be due to their growth-promoting effect on plants. Therefore, this product confuses end users because they expect anti-pest activity from these products. As a result, they are often ineffective, which helps diminish microbial biocontrol agents' reputation. Even though many strains blur the distinction between plant protection products and biopesticide/biofertilizers, strict regulation is required to ensure the efficacy of biopesticide microorganisms and prevent their misuse as plant protection products. Presently, only a few genera, species, and strains of BCAs ( Coniothyrium minitans , Gliocladium catenulatum , Pseudomonas chlororaphis and spp., Streptomyces griseovirides and Streptomyces lydicus , and Trichoderma asperellum , T. atroviride , and T. harzianum ) are registered against some soil-borne pathogens. Similarly, Bacillus firmus  and Purpureocillium lilacinum are the only BCAs approved for use against nematodes [ 35 ].

Experts have encountered great difficulties in developing various BCA products, in addition to the cost and scalability challenges of BCA. Many alternative solutions, such as those based on fermentation or pheromones, are prohibitively expensive to manufacture, providing customers with a little financial incentive to switch away from using known BCA-containing products. As a result, several companies are looking into novel ways to reduce production costs. Due to the low barriers to entry and high market attractiveness in this domain, hundreds of companies, ranging from major CP firms to many mid-tier firms, engage in the BCA and bio-stimulants industries. Many new businesses emerge due to the influx of venture capital, but they frequently lack the funds to register their company, develop their products, and enter the market [ 34 ]. To register a single strain for commercial usage, firms would have to conduct extensive, statistically significant efficacy trials for each crop/disease in each zone. This limitation has resulted in a dearth of products for the biological control of insect pests and diseases in Europe and Asia. As was previously stated, only a handful of these products have been approved for use by European growers [ 35 ].

Opportunities

Biological control is a cost-effective, eco-friendly, and long-term solution for crop protection against biotic stresses. Progressive farmers increasingly use the conservation and management of endangered species of biocontrol microorganisms, among other biologicals, to combat plant diseases [ 36 ]. The most successful approach to biological management for conservation objectives, according to Kean et al. [ 37 ], is to concentrate on the most critical aspects of natural enemy ecology. According to Heimpel and Mills [ 38 ], there are two strategies to boost natural enemy effectiveness: (1) changing the habitat so that natural enemies benefit at the expense of pests or (2) decreasing the detrimental effects of pesticides on natural enemies. Furthermore, the significance of biological control conservation in developing countries has been emphasized [ 31 , 39 ]. Numerous microorganisms have been shown to be effective against soil-borne diseases and nematodes over the last 50 years. Among these are the active ingredients in at least one biopesticide that is already on the market. Even though several of these strains were developed a few years ago, none have achieved widespread commercial success due to competition from synthetic chemical fumigants, which are often more cost-effective, easier to apply, have a wider spectrum of activity, and are highly effective. Since the ban on methyl bromide and other chemicals, there has been a revived interest in microbial biocontrol agents against soil-borne diseases. These agents operate best in conjunction with other agronomic practices or resistant/tolerant plant varieties. The mechanism of action of microbial biocontrol agents against plant pathogens includes direct antibiosis, hyper-parasitism, resistance induction, and competition for space and nutrients.

In addition, researchers are investigating the role of non-pathogenic beneficial rhizobacteria in increasing plant resistance to pathogens, a process known as induced systemic resistance (ISR). Plant pathogen infection can result in systemic acquired resistance (SAR) [ 4 , 40 , 41 , 42 , 43 ]. Some microorganisms (such as Bacillus spp ., Pseudomonas spp., Acinetobacter calcoaceticus ; Azotobacter spp., Azospirillum spp., Mesorhizobium , Bradyrhizobium , Burkholderia ) act as bio-stimulators by producing indole-acetic acid, nitrogen fixation, P-solubilizing, siderophore, HCN production, 1-aminocyclopropane-1-carboxylate (ACC) deaminase, degrading organic matter to improving the plant growths & yields, controlling disease & pest and maintaining soil health's [ 44 , 45 , 46 , 47 , 48 ]. Furthermore, soil and plant microbiomes can act as inoculants, aid in nutrient absorption, biocontrol products, help protect plants from pests and diseases, or both. Some soil amendments may be required to ensure beneficial microbes' survival. Perhaps "probiotics" can be identified to maintain plant microbiomes healthy [ 49 , 50 , 51 , 52 , 53 ].

Priorities for research in exploring of biocontrol agents

Basic biological research, particularly in taxonomy, ecology, and behavior, has tremendously aided procedures employed in the exploration, selection, and risk evaluation of biological control agents. However, some questions remain unanswered in the field of biocontrol, such as the lacking efficacy in profiling plant-associated microbial bio-controlling agents, the lack of an overall understanding of a pathogen's biology, and the epidemiology of the resulting disease, which hinder the development of disease and pest management strategies. Therefore, scientists and researchers must keep the following goals in mind as they investigate emerging issues in the field in order to establish an eco-friendly bio-control strategy.

▪ Exploration of a new generation of biocontrol agents with higher efficacy, high productivity in fermenters, long shelf life and the ability to be stored at room temperature, and high compatibility with other control methods

▪ Standardizing of identification of BCA protocol against soil-borne disease & pest 

▪ Population genetics research presents opportunities to better understand how the impact of biological control can be optimized.

▪ Improving microbiological control by integrating several strains of the same genus specie, or several genus-specie

▪ Increase our understanding of microbial biocontrol agents' potential against other soilborne pathogens beyond those listed on labels, as well as their potential use with carriers that can increase survival in soil, in order to demonstrate their environmental safety.

▪ Exploring cutting-edge genomic tools like CRISPR genome editing can reduce fewer desirable traits in biological control agents and insert new desirable characteristics such as insecticide resistance.

▪ Implementation of IPM strategies which include the use of microbial biocontrol agents with other management strategies 

▪ Optimize and reduce the cost of production of BCA by improving the technologies of fermentation or use of low-cost carrier substrates for BCA

▪ To protect food and ornamental crops from pathogens, it is important to encourage and assist businesses in registering microbial products that meet the criteria of 'low-risk substances' either by expediting the registration of low-risk substances or by providing subsidies to farmers who choose low-risk substances). 

Conclusions

We believe that understanding the effective biocontrol agents and their combined impact on emerging pathogenesis and cytotoxicity requires a holistic approach of resilience and responsiveness. Furthermore, it is critical to learn about eco-friendly tools and identify viable crop protection management practices in organic and sustainable farming. Therefore, researchers are encouraged to submit papers or reviews addressing the aforementioned challenges, opportunities, and priorities for BCA research, and we also encourage researchers to submit research papers or reviews dealing with these areas: how biocontrol microbes regulate plant defense mechanisms?; deploy biocontrol actions in plants and offer new strategies for controlling plant pathogens and pests; how do plants interact with beneficial microbes while restricting pathogens?; engineering biocontrol microbial consortium and their efforts to improve, facilitate, and maintain long-term pest and disease management, as well as plant growth, human risk evaluation of rhizospheric and entomopathogenic microbes to be employed as plant pest control research on the topic of Biocontrol strategies: An Eco-smart tool for integrated pest & diseases management.

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Acknowledgements

The authors would like to thank the senior editor of BMC Series Journals for her highly valuable input during the preparation of the collection. The authors would also like to thank the ICAR-Directorate of Onion and Garlic Research, Rajgurunagar, India; the Department of Botany, Savitribai Phule Pune University, Pune, India; the ICAR-Indian Institute of Vegetable Research, Varanasi, India; and the University of Zurich, Switzerland, which form a large part of setting the scene behind this collection.

DKJ is supported by UGC-Dr. D.S. Kothari Postdoctoral Fellowship (No.F.4 − 2/2006 (BSR)/BL/20–21/0082) to carry out the research work at Department of Botany, Savitribai Phule Pune University, Pune, India and Science and Engineering Research Board (SERB), Government of India awarded national postdoctoral fellowship (PDF/2021/004263) to RK to conduct research at ICAR-IIVR, Varanasi. The funder did not play any role in the commentary design, preparation, writing, or approval.

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DKJ drafted the first version of the commentary with significant input, comments, and revisions from SJG, SPS, RK, ANV, & ABA. All authors have approved the final version.

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Jaiswal, D.K., Gawande, S.J., Soumia, P.S. et al. Biocontrol strategies: an eco-smart tool for integrated pest and diseases management. BMC Microbiol 22 , 324 (2022). https://doi.org/10.1186/s12866-022-02744-2

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DOI : https://doi.org/10.1186/s12866-022-02744-2

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• Plant disease
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• Entomopathogenic microorganism pathogenesis related proteins (PRs)
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Advancing Biological Control Strategies for Sustainable Pest Management in Agricultural Systems

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Biological control, a cornerstone of integrated pest management (IPM), represents a sustainable and ecologically conscious approach within modern agricultural systems. This strategy harnesses the natural interactions between living organisms to regulate pest populations and maintain a balanced ecosystem. Beneficial organisms such as predators, parasitoids, and pathogens are employed to control pest species, reducing the reliance on synthetic pesticides and minimizing their potential negative impacts on the environment, non-target organisms, and human health. By introducing these natural enemies into agricultural landscapes or enhancing their populations, biological control offers long-term pest suppression while promoting biodiversity and ecosystem resilience. Through careful understanding of the ecological dynamics and intricate relationships among organisms, biological control stands as a promising avenue to enhance crop protection and yield stability while fostering a harmonious coexistence between agriculture and the natural world. Agricultural systems worldwide face significant challenges in managing pest populations while minimizing the negative environmental impacts of traditional chemical pesticides. Prolonged and indiscriminate pesticide use has led to pest resistance, harm to non-target organisms, and soil and water pollution. Achieving sustainable agriculture requires effective pest management strategies that balance crop protection with ecological conservation. Recent advances offer promising avenues for achieving sustainable agriculture. The dynamic field of pest management is witnessing a transformative shift towards sustainable solutions. Biological Control Agents (BCAs) have emerged as a formidable force, leveraging predatory insects, parasitoids, and microbial pathogens to curtail pest populations. Recent breakthroughs in mass rearing, formulation, and delivery methods amplify their effectiveness for large-scale implementation. Augmentation and Conservation strategies underscore the importance of releasing natural enemies while orchestrating habitat preservation to ensure persistent pest suppression. Conservation biological control takes root in enhancing the very habitats that nurture beneficial insects. Unveiling the intricate dance of plant-soil-insect microbiome interactions unveils a realm of possibilities for targeted manipulation, fostering plant resilience against pests. Biotechnological Interventions, powered by RNA interference (RNAi) technology, herald a new era of precision with pest-specific biopesticides, sparing beneficial insects from collateral damage. In a world increasingly shaped by climate change, adopting Climate-Smart Approaches becomes pivotal. Acknowledging climate's influence on pest dynamics, integrating adaptive strategies cements the fortitude of biological control systems. As agriculture navigates the future, these pathways illuminate a promising journey toward harmonizing crop protection with environmental sustainability. Entomopathogens are microorganisms that are pathogenic to arthropods such as insects, mites, and ticks. Several species differ significantly in their biology and behavior, and hence in their ability to control the population of enemies in each environment. The proper use of entomopathogens requires a good knowledge of the biological cycle of enemies. Entomopathogens such as bacteria, fungi, nematodes, and viruses infect a variety of arthropod pests and play an important role in their management. The pathogenicity caused by the entomopathogens is not the same in all insects and differs even at each stage of the insect. It is usually larger in the young stages of the insect, especially in the larval stage. The point of entry or growth of a pathogen varies depending on the insect and the entomopathogen. The entomopathogens (usually viruses and bacteria) enter via the oral route, while fungi can invade their host from the insect cuticle. Some entomopathogens are mass-produced in vitro (bacteria, fungi) or in vivo (viruses) and sold commercially. This Research Topic focuses on advancing innovative biological control strategies within the Integrated Pest Management (IPM) framework for sustainable pest management in diverse agricultural systems. It aims to unite research addressing challenges posed by traditional pesticides while promoting ecological conservation and crop protection. Themes to be Explored: 1. Biological Control Agents (BCAs): Manuscripts should delve into optimizing predatory insects, parasitoids, and microbial pathogens as BCAs. Research on mass rearing, formulation, and application methods is encouraged. 2. Augmentation and Conservation: Contributions should focus on augmenting natural enemies through releases and habitat management, as well as enhancing habitats of beneficial insects. 3. Microbiome-Mediated Control: Manuscripts should explore microbiome interactions for improved pest management and plant health. 4. Climate-Resilient Strategies: Addressing climate change impact on pest dynamics and developing adaptive control strategies. 5. Policy and Socio-Economic Implications: Discussing policy recommendations, economic incentives, and socio-cultural factors influencing the adoption of biological control strategies by farmers. Types of Manuscripts: 1. Original Research Articles: Presenting novel findings on biological control efficacy, mechanisms, and application in different agricultural contexts. 2. Reviews: Synthesizing recent biological control advances, highlighting challenges, and proposing future directions. 3. Methodology Papers: Detailing innovative techniques for developing and implementing biological control strategies. 4. Case Studies: Describing successful biological control implementation in specific crops/regions, including lessons learned and best practices.

Keywords : biological control, integrated pest management, sustainable agriculture, natural enemies, predators, parasitoids, pathogens, pest suppression, biodiversity, ecosystem resilience, crop protection, ecological dynamics, agricultural systems

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Bacteria as Biological Control Agents of Plant Diseases

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Biological control is an effective and sustainable alternative or complement to conventional pesticides for fungal and bacterial plant disease management. Some of the most intensively studied biological control agents are bacteria that can use multiple mechanisms implicated in the limitation of plant disease development, and several bacterial-based products have been already registered and marketed as biopesticides. However, efforts are still required to increase the commercially available microbial biopesticides. The inconsistency in the performance of bacterial biocontrol agents in the biological control has limited their extensive use in commercial agriculture. Pathosystem factors and environmental conditions have been shown to be key factors involved in the final levels of disease control achieved by bacteria. Several biotic and abiotic factors can influence the performance of the biocontrol agents, affecting their mechanisms of action or the multitrophic interaction between the plant, the pathogen, and the bacteria. This review shows some relevant examples of known bacterial biocontrol agents, with especial emphasis on research carried out by Spanish groups. In addition, the importance of the screening process and of the key steps in the development of bacterial biocontrol agents is highlighted. Besides, some improvement approaches and future trends are considered.

1. Introduction

Plant pathogens constitute a great threat to agricultural and forestry production since they cause diseases with important economic and environmental impact [ 1 , 2 ]. Currently, their effect has worsened due to globalization of markets and global climate change that facilitate the appearance of emerging diseases and their rapid spread [ 3 ]. New trends in crop protection have been oriented toward a reduction of reliance on conventional pesticides together with the compulsory implementation of integrated pest management (IPM) principles program addressed in the regulations of different countries [ 4 , 5 ]. Consequently, the interest in effective and sustainable alternative strategies to conventional pesticides has increased. Biological control is regarded as a promising alternative and a wide array of microbial biocontrol agents (BCA) have been developed in the past decades for the management of fungal and bacterial diseases. Some of the most intensively studied are bacteria belonging of the genus Pseudomonas spp., Bacillus spp., and Streptomyces spp., that have been already registered as commercial products and marketed. Nowadays, in EU there are 13 bacterial-based biocontrol agents (BCA) registered as biopesticides for the control of bacterial and fungal diseases ( Bacillus amyloliquefaciens strains: QST 713, AH2, MBI 600, FZB24 and IT 45, Bacillus amyloliquefaciens subsp. plantarum strain D747, Bacillus firmus I-1582, Bacillus pumilus strain QST 2808, Bacillus subtilis strain IAB/BS03, Pseudomonas sp. strain DSMZ 13134, Pseudomonas chlororaphis strain MA 342, Streptomyces K61 and Streptomyces lydicus strain WYEC 108) ( https://food.ec.europa.eu/plants/pesticides/eu-pesticides-database_en , accessed on 1 June 2022). However, efforts are still required to increase the commercially available microbial biopesticides for plant disease management [ 6 ].

The efficacy of a bacterial biocontrol agent against plant diseases depends on the microbial agent (mechanism of action, conditioning, dose, methods of application), plant pathogens targets (sensitivity), host (cultivar type, physical properties), and environmental conditions (biotic and abiotic factors, chemical residues, nutrient availability, temperature, moisture) [ 7 ]. Numerous interactions may affect the efficacy of biocontrol such as the variability from plant to plant, orchard, and year, and often lack of efficacy and inconsistent field performance have been reported. Therefore, it is necessary to know the efficacy and consistency of biological control in comparison to standard chemical fungicide and bactericide treatments under sufficiently wide production conditions in orchards representing different environments and agricultural practices [ 8 , 9 ].

Bacterial biocontrol agents use a great variety of mechanisms to protect plants from pathogen infections. They may use one or a combination of mechanisms to prevent or reduce plant disease, interacting directly or indirectly with the pathogen [ 10 , 11 ] ( Figure 1 ). BCA can interact directly with the pathogen through the secretion of antimicrobial compounds, interfering with the pathogen virulence and competing for nutrients and space. Many BCA synthesize and release metabolites such as lipopeptides, bacteriocins, antibiotics, biosurfactants, cell-wall degrading enzymes or microbial volatile compounds which have antimicrobial activity by reducing growth or metabolic activity of pathogens. BCA may also interfere with the quorum sensing (QS) system of the pathogens, enzymatically degrading or inhibiting the synthesis of signal molecules used to initiate infections. For instance, producing QS inhibitors such as lactonases, pectinases, and chitinases that degrade QS signal molecules impairing pathogen infection and reducing the symptoms of plant diseases [ 12 ]. Moreover, BCA can diminish pathogen infection pressure through competitive exclusion over pathogens by reducing their growth without killing them. Highly competitive bacterial BCA may colonize and survive in the infection site and have a more efficient nutrient uptake system than the pathogen, such as low-molecular-weight siderophores with affinity for ferric iron. Besides direct interactions, BCA can protect plants indirectly, by triggering the defense response or promoting plant growth [ 10 , 11 , 13 ]. They may enhance host defense mechanism eliciting systemic resistance. This results in an accumulation of structural barriers and triggers many biochemical and molecular defense responses in the host, conferring a protective system against a wide range of pathogens. Moreover, BCA can promote plant growth by enhancing mineral and water absorption or producing plant growth stimulating compounds, such as hormones, and thereby improving plant health and fitness. In many cases, various mechanisms are involved in the complex interactions between plants, BCA, and pathogens. Therefore, identifying the mechanisms responsible for biocontrol is a great challenge. Understanding the mode of action responsible for the protective effect of a BCA will facilitate the optimization of biocontrol and allow the establishment of optimal conditions for the interaction between the BCA, the pathogen, and the host, and the design of appropriate formulations and methods of application to enhance plant health and sustainable agriculture.

Overview of the direct and indirect mechanisms of biocontrol involving interaction between bacterial biocontrol agent, pathogen, and plant (created with BioRender.com).

This review shows some relevant examples of known bacterial BCA, and presents their main modes of action, including details concerning the mechanisms and molecules involved in the biocontrol activity with especial emphasis on research carried out by Spanish groups. In addition, the importance of the isolation, screening process, characterization of the key steps in the development of BCA is highlighted. Moreover, some improvement approaches and future trends are considered.

2. Bacteria as Biological Control Agents of Plant Diseases

A wide variety of bacterial genera, including Agrobacterium, Alcaligenes, Arthrobacter, Bacillus, Enterobacter, Erwinia, Pseudomonas, Rhizobium, Serratia, Stenotrophomonas, Streptomyces, and Xanthomonas have been described to have plant disease protection activity against fungal and bacterial pathogens. These bacteria can use multiple mechanisms implicated in the limitation of plant pathogens development. These mechanisms of action include colonization of infection sites and competitive exclusion of the pathogen, antagonistic activity based on the secretion of highly active antimicrobials such as antibiotics or cell wall lytic enzymes and induction of plant resistance [ 7 , 14 , 15 ].

Several bacterial BCA of bacterial and fungal pathogens have been developed in research carried out within the framework of Spanish groups and some examples are highlighted ( Table 1 ).

Selected bacterial biocontrol agents 1 of plant diseases.

1 Only examples of studies performed by Spanish groups are selected. 2 BFV, bioprotection of fresh fruits and vegetables; GTD, grapevine trunk diseases; PBF, phytopathogenic bacteria and fungi; PF, postharvest fungi; Ac, Alternaria citri ; B, Botryosphaeria sp.; Bc, Botrytis cinerea ; Cg, Colletotrichum gloesporioides ; Ea, Erwinia amylovora ; Fa, Fusicoccum aromaticum ; Fop, Fusarium oxysporum f. sp. pisi; Gt, Gaeumannomyces tritici; Lt, Lasidiplodia theobromae ; Mp, Macrophomina phaseolina ; Mf, Monilia fructicola ; Ml, Monilia laxa ; Pc, Penicillium crustosum ; Pp, Phomopsis perse ; Pc, Phytophthora cactorum ; Pf, Podosphaera fusca ; Ps, Pseudomonas syringae ; Psk, Pseudomonas syringae pv kiwi; Rn, Rosellinia necatrix ; Rs, Rhizoctonia solani ; Vd, Verticillium dahliae ; Xa, Xanthomonas arboricola ; Xf, Xanthomonas fragariae . 3 Ab, antibiosis; CE, competitive exclussion; IR, induced resistance; NC, nutrient competition.

2.1. Pseudomonas spp.

Fluorescent pseudomonads are ubiquitously present in plant environments and possess several relevant traits for their effectiveness in the reduction of plant diseases. These traits include a high ecological fitness, a strong antagonistic activity toward various plant pathogens, and a potent ability to trigger an immune reaction in plant.

Many Pseudomonas spp. are efficient colonizers of the plant surface (rhizosphere and phyllosphere) and the endosphere. They can use many plant exudates as nutrients and have a high growth rate, which are prerequisites to efficiently compete with other microorganisms for space and nutrients in the plant environment [ 37 , 38 , 39 ]. For example, the activity of P. fluorescens EPS62e and P. pseudoalcaligenes AVO110 in the reduction of Erwinia amylovora or Rosellinia necatrix infections, respectively, is based on their strong fitness in colonizing plant tissues as they have higher growth potential and nutrient use efficiency than the target pathogens [ 29 , 33 ]. In addition, competition for limited nutrients has been described as an important mechanism of Pseudomonas spp., but it is only relevant when the concentration of a given limited nutrients is low, such as in the biological control of Pythium ultimum by P. fluorescens 54/96 [ 40 ] or in the case of siderophore-mediated competition for iron in the reduction of Fusarium wilt of carnation by P. putida WCS358 [ 41 ].

Another relevant trait of Pseudomonas spp. is that they are major producers of bioactive metabolites, such as antibiotics, cyclic peptides, or enzymes that play important ecological roles. Specifically, they produce different antimicrobial compounds such as phenazines, phloroglucinols, dialkylresorcinols, pyoluteorin, and pyrrolnitrin, whose involvement as a mechanism of action in biological control has been well documented [ 38 , 42 ]. Phenazines such as phenazine-1-carboxamide (PCN) or phenazine-1-carboxylate (PCA) are nitrogen-containing heterocyclic compounds with broad antifungal and antibacterial activities. These compounds are involved in the reduction of fungal pathogens infections of plants. For example, PCN produced by P. chlororaphis subsp. aurantiaca strain Pcho10 shows strong inhibitory activity against Fusarium graminearum [ 43 ] and PCA produced by P. fluorescens EPS894 inhibits Phytophthora cactorum in strawberry plants [ 30 ]. The phloroglucinols are phenolic broad-spectrum antibiotics produced by a wide variety of bacterial strains. Specifically, 2,4-diacetyl phloroglucinol (DAPG), produced by different strains of Pseudomonas spp., has a broad-spectrum action, and contributes to the biological control of plant disease, especially soil-borne plant diseases [ 28 , 44 ]. Dialkylresorcinols exhibit antifungal and antibacterial activities such as the compound 2-hexyl-5-propyl resorcinol produced by P. chlororaphis PCL 1606 is responsible for the biocontrol of R. necatrix [ 27 ]. Pyrrolnitrin have also been involved in the biocontrol of the Fusarium head blight by P. chlororaphis G05 [ 45 ]. Pyoluteorin, as well as the volatile compound hydrogen cyanide are other compounds produced by different strains of Pseudomonas spp. that have been involved in the biocontrol of some pathogens [ 46 ].

Moreover, pseudomonads produce cyclic lipopeptides (CLPs) that are amphiphilic molecules containing chains of 7–25 aminoacids of which several form a lactone ring coupled to a fatty acid tail. Many of the CLPs are biosurfactants, which can damage cell membranes, thereby causing leakage and cytolysis and are a common feature of both plant beneficial and pathogenic bacteria [ 46 , 47 ]. Interestingly, some of them such as orfamides synthesized by P. protegens have antimicrobial activity against a variety of organisms, including the pathogenic oomycetes Pythium and Phytophthora , and the fungus Rhizoctonia [ 48 ]. Other examples that show antifungal activity are the cyclic depsipeptide viscosinamide produced by P. fluorescens DR54 [ 49 ] or the peptide tensin produced by P. fluorescens 96.578 [ 50 ].

Pseudomonads can also produce lytic extracellular enzymes such as chitinases, β-1,3 glucanases, cellulases that have important roles in biocontrol activity by their degradative activities of cell wall compounds, such as chitin, glucan, and glucosidic bridges. For example, hydrolytic enzymes produced by Pseudomonas sp. have in vitro antifungal activity against Pythium aphanidermatum and Rhizoctonia solani and promote growth in chickpea [ 51 ].

Pseudomonas spp., can trigger defense responses of host plants through different pathways, conferring plants with resistance to multiple pathogens. In many cases they confer resistance to plant upon the activation of induced systemic resistance (ISR) that involves activation of immune response and priming state for a more efficient activation of defenses. For example, in Vitis , P. fluorescens PTA-CT2 induces ISR to Plasmopara viticola and Botrytis cinerea that depends on the activation of SA or JA and ABA defensive pathways [ 52 ]. In another case, the biocontrol endophytic bacterium Pseudomonas simiae PICF7 induces systemic defense responses in aerial tissues upon colonization of olive roots [ 31 , 32 ]. In addition, some compounds such as CLPs or phenazines have been reported to trigger defense responses in plants. For example, massetolide A of P. fluorescens enhanced resistance to infection by Phytophthora infestans in tomato plants [ 53 ] and phenazines from Pseudomonas sp. CMR12a induced systemic resistance on rice and bean [ 54 ].

2.2. Bacillus spp.

Bacillus species are among the most exploited beneficial bacteria as biopesticides. They are widely distributed in several habitats such as soil and plant surfaces, have broad physiological ability and capability to form endospores that confers resistance to adverse environmental conditions. They can develop antagonism against a wide range of bacterial and fungal plant pathogens. The most remarkable trait of Bacillus spp. is the ability to produce a wide variety of bioactive compounds valuable for agricultural applications, including metabolites with antimicrobial activity, surface-active, and implicated in the induction of plant defense responses [ 55 , 56 ].

Bacteriocins and bacteriocin-like substances are ribosomally synthesized peptides that act against target cells by interfering with the synthesis of the cell wall or by forming pores in the cell membrane. Bacillus spp. produce several bacteriocins with antimicrobial activity such as amylolysin, amylocyclicin, amysin, subtilin, subtilosin A, subtilosin B, thuricin [ 57 ]. Some of them have been involved in biocontrol of plant pathogens. For example, Bac-GM17 produced by B. clausii GM17 have activity against Agrobacterium tumefaciens [ 58 ] or thuricin Bn1 from B. thuringiensis subsp. kurstaki Bn1 against Pseudomonas savastanoi and Pseudomonas syringae [ 59 ].

Cyclic lipopeptides (CLPs) are non-ribosomally synthetized amphiphilic compounds, composed of a fatty acid tail linked to a short oligopeptide which form a macrocyclic ring structure that are widely spread in Bacillus spp. The most important CLPs produced by Bacillus are represented by iturins, fengicins, and surfactins. They interact with cell membrane of target pathogens forming pores and leading to an imbalance in transmembrane ion fluxes [ 60 ]. There are several examples of Bacillus spp. strains producing CLPs, that are responsible for the antifungal activity that protect plants from diseases. The fengycin, iturin A, and surfactin produced by B. amyloliquefaciens PPCB004 and bacillomycin, fengycin, and iturin A produced by B. subtilis UMAF6614 and UMAF6639 are key factors in the antagonism against fungal pathogens [ 16 , 18 ]. In addition, Bacillus strains producing CLPs have also antibacterial activity such as B. amyloliquefaciens A17 (currently B. velezensis ) that produces bacillomycin, fengycin, iturin, and surfactin which act synergistically against several bacterial plant pathogens [ 19 , 20 ], or B. amyloliquefaciens KPS46 that produces surfactin, required to reduce infections by Xanthomonas axonopodis pv. glycines [ 61 ]. In many cases, lipopeptides and other peptides or volatile organic compounds (VOCs) act in a synergistic manner to improve their activity. For example, B. amyloliquefaciens CPA-8 produces fengycin and VOCs that are involved in the antifungal activity against Monilinia and Botrytis [ 17 ]. Besides their antimicrobial activity, some of these compounds act indirectly as elicitors of defense mechanism in the host plant or play an important role in favoring colonization [ 62 ].

Hydrolytic enzymes such as chitinases, chitosanases, glucanases, cellulases, lipases, and proteases, are also extensively produced by Bacillus spp. strains. These compounds efficiently hydrolyze the major components of the fungal and bacterial cell walls and have been involved in plant pathogen suppression. For example, a protease produced by B. amyloliquefaciens SP1 showed efficacy in biocontrol of Fusarium oxysporum [ 63 ] and the hydrolase activity (protease, chitinase, cellulase, glucanase) was identified as the key factor of B. velezensis in controlling pepper gray mold caused by Botrytis cinerea [ 64 ].

Various Bacillus spp. strains can elicit ISR in different plants and confer an enhanced defense mechanism against a range of pathogens. Several studies have shown that VOCs and CLPs, such as surfactin and fengycin, are involved in the immune response of plants elicitation [ 65 , 66 ]. For example, B. amyloliquefaciens FZB42 produced secondary metabolites (surfactin, fengycin, and bacillomycin D) that trigger plant defense gene expression and contribute to lettuce bottom rot reduction [ 67 ]. In another example, Bacillus subtilis OTPB1 increased the levels of growth hormones and defense-related enzymes in tomato, conferring protection against early and late blight [ 68 ].

2.3. Other Relevant Bacteria as BCA

There are other relevant species/strains which can be used to develop microbial biopesticides. Some are distributed among the Gram-negative bacteria of the families Rhizobiaceae, Enterobacteriaceae, and Xanthomonadaceae. Others can be found among Gram-positive bacteria such as Lactobacillaceae, Leuconostocaceae, and Streptomycetaceae [ 69 ]. Some examples, since they reduce plant pathogenic bacteria and fungi infections, include species of Streptomyces spp., Pantoea spp., and Lactobacillus spp.

Streptomyces spp. is one of the most studied genus of bacteria, since they produce bioactive compounds that inhibit plant pathogens in vitro and are effective in the controlling various bacterial and fungal plant diseases [ 70 ]. Examples of such metabolites include macrolide benzoquinones, aminoglycosides, polyenes, and nucleosides. Streptomyces strains are also known for their ability to produce extracellular enzymes active in fungal cell wall degradation. These hydrolases may be responsible for the mycoparasitic potential of some strains and the limitation of plant diseases, such as in the strains Streptomyces CBQ-EA-2 and CBQ-B-8 that have chitinolytic, cellulolytic, and proteolytic activity and reduced Macrophomina phaseolina and Rhizoctonia solani infections in Phaseolus vulgaris [ 34 ]. Other bioactive metabolites are produced, including VOCs, as signaling molecules to regulate plant growth and immunity in response to biotic and abiotic stresses. In addition, some strains can limit plant disease development through the induction of systemic resistance (ISR) in plants. ISR elicited by Streptomyces strains occurs via the activation of the jasmonic acid/ethylene and salicylic acid pathways. For example, S. lydicus M01 treatment reduced the reactive oxygen species (ROS) accumulation and increased the activities of antioxidases related with ROS scavenging, which indicated an enhanced resistance of cucumbers against Alternaria alternata foliar disease [ 71 ]. Predominantly, these bacteria are obtained from the soil, and from the endosphere and rhizosphere of plants. As an example, Streptomyces sp. endophytic strain VV/E1 and rhizosphere VV/R1 and VV/R4 strains exhibited antifungal activity and reduced nursery fungal graft infections on grapevine plants [ 35 ].

Many strains of Pantoea spp. have aptitudes as BCA because they are ubiquitous and produce antimicrobial compounds. Biopesticides based on Pantoea spp. are registered and commercially available in Canada, USA, and New Zealand. They have biocontrol activity through various mechanisms, including competitive colonization, production of antimicrobials, and/or induction of host systemic defense. Some strains of Pantoea species have been shown to target a wide spectrum of plant pathogens including bacteria, fungi, and oomycetes via secretion of antimicrobial compounds such as pantocins, herbicolins, microcins, and phenazines [ 72 , 73 ]. Other strains such as P. agglomerans EPS125 or strain CPA-2 require direct cell-to-cell interaction to combat postharvest fungal pathogens, without relying on the production of antibiotic substances or nutrient competition [ 24 , 25 , 26 ]. In another example, Pantoea species can also produce N-acyl-homoserine lactone (AHL), affecting quorum sensing in pathogens which, coupled with promoting environmental fitness in plants, may contribute to limit pathogen development [ 74 ].

Lactic acid bacteria (LAB) are good candidates as BCA because they include some strains categorized as Generally Regarded as Safe (GRAS) by the U.S. Food and Drug Administration (FDA) and as having Qualified Presumption of Safety (QPS) status by European Food Safety Authority (EFSA) and have been widely reported as biopreservatives of vegetables and fruits [ 23 ]. LAB show antimicrobial activity due to the production of one or more antimicrobial metabolites. These include organic acids, carbon dioxide, diacetyl, hydroxide peroxide and proteinaceous compounds such as bacteriocins and antifungal peptides. They may also exclude pathogens by pre-emptively colonizing plant tissues susceptible to infection, by competition for nutrients and space, or by inducing defense responses in plants. For example, L. plantarum PM411 and TC92 are effective in preventing bacterial plant diseases. Their broad spectrum of antagonism against plant pathogenic bacteria is based on antimicrobial metabolites, together with the reduction of infections by inhibition of pathogen population on plant surfaces [ 21 , 22 , 75 ]. Moreover, Weissella cibaria TM128 exhibited antimicrobial activity and prevented blue mold, mainly due to the production of organic acids and hydrogen peroxide [ 36 ].

3. Bacterial Biocontrol Agent’s Development—Flowchart of Actions

The development of bacterial BCA requires several steps ( Figure 2 ). It includes: (i) The isolation and selection of strains by means of screening methods able to analyze a high number of microorganisms; (ii) the characterization of the BCA, including the identification, the determination of phenotypic and genotypic traits, and the mechanisms of action, biocontrol efficacy in pilot tests and improvement; (iii) mass production and an appropriate formulation, which allow increasing biocontrol activity and ensuring its stability. Finally, the development of a monitoring system to detect and quantify the BCA in the environment and to make more extensive toxicology tests or environmental impact studies with the aim to register for use is required.

Flowchart of actions for bacterial biocontrol agents development.

3.1. Isolation and Screening for Strain Selection

The first stage of BCA development consists of the isolation and screening of isolates able of limiting the development of the targeted plant pathogen and reducing disease levels. Proper sampling at adequate niches can increase the probability of obtaining useful strains, therefore careful selection of the origin of samples, culture media composition, and enrichment-isolation techniques is very decisive [ 8 ]. Bacterial antagonists that prevent or limit disease development are naturally present in the plant environment (phyllosphere, rhizosphere, and endosphere) or in bare soil. Different habitats can be used as suitable sources to obtain candidates as BCA.

For example, samples may be taken from suppressive soils or healthy plants from epidemic areas, where there is evidence of presence of beneficial microorganisms, or near the pathogen infection site [ 8 , 9 ]. In addition, other habitats different from the plant environment can also allow to obtain beneficial bacteria. As the presence of microorganisms with suitable properties as BCA is relatively rare in a strain collection, the isolation of a high number of candidates is recommended. The choice of the isolation technique using selective and enrichment culture media allows for the successful isolation of microorganisms of interest. However, this approach restricts the type of microorganisms obtained and few bacteria genera have been systematically evaluated as BCA. Another approach deals with the use of molecular markers to prospect BCA candidates by means of the specific detection of genes involved in the biocontrol and can be used as a good strategy to increase the efficiency of screening procedures [ 7 , 49 , 50 ]. The advances in genome sequencing and annotation, and the understanding of the mechanisms of action of BCA have greatly increased the availability of marker genes as tools for the screening [ 76 ]. Moreover, considering that a wide array of bacteria from different taxonomic group that studies the structure and function of plant microbiome have been identified [ 77 , 78 ], in-depth study of genetic diversity of microbial communities associated with plants can allow finding new bacteria with relevant traits related to biocontrol which can extend the candidates for plant diseases management [ 79 ].

Once a collection of isolates has been made, the putative BCA will be selected based on their attributes. The screening for appropriate candidates is a critical step in the development of novel bacterial BCA and determines the type of microorganism selected [ 7 , 9 , 80 ]. Rapid-throughput in vitro assays are widely used. In these assays, the target pathogen and candidate biocontrol agents are grown together in solid or liquid media to test for direct reduction of pathogen growth. These assays are fast, reproducible, and reliable, and allow the analysis of many isolates. However, they only permit the selection of bacteria with antagonistic activity, and they may not identify microorganisms with other mechanisms of action such as competitive exclusion or induction of plant resistance [ 10 , 81 ]. Screening procedures such as small-scale whole-plant bioassays in which pathogen and antagonists interact with the host in controlled conditions allow the selection of microorganisms with other mechanisms of action and have a good correlation with biocontrol efficacy in the field. However, these assays are time-consuming and require significant number of resources. The development of ex vivo bioassays on seeds, detached leaves, flowers, and fruits reduces plant material size and permits faster, reliable, and efficient screening [ 82 , 83 ]. A multi-pathogen approach is recommended to select strains with a broad spectrum of activity [ 74 , 84 ].

3.2. Characterization of Selected Strains

The deep characterization of the selected strains is an important stage of BCA development since it provides relevant information about strains for their exploitation as biopesticides. The identification, and phenotypic and genotypic characterization of the strains reveals key attributes in their activity as biocontrol agents. Some of these traits include the synthesis of compounds related to the antimicrobial activity such as enzymes, antibiotics, bacteriocins, or toxins that have detrimental activity against other microorganisms, or to their ability to trigger an immune reaction in plant tissues. Moreover, other traits contribute to the ability of a bacterial strain to colonize plant environment such as the efficient use and uptake of nutrients from exudates (amino acids, organic acids, sugars), motility (flagella), fast growth rate, ability to synthesize amino acids and vitamins, and presence of different structures for adhesion to plant surfaces, such as pili, fimbriae, major outer membrane proteins, or the O-antigen chain of lipopolysaccharides [ 85 , 86 ]. Understanding the traits that are involved as the mechanism of action of a BCA may help finding optimum conditions for implementing biocontrol in each pathosystem. However, the assessment of the mechanisms is a complex and difficult task because of the need of prospective studies to reveal the implication of a given process (e.g., antibiosis, nutrient competition, host colonization, induction of plant defense) and because, in most cases, there are several mechanisms involved and the importance of each one depends on the particular biotic and abiotic conditions.

Nowadays, the genome sequencing of BCA and its comparison with related published genomes will provide a framework for further functional studies of their colonization of plant environment competence and biocontrol effectiveness [ 87 ]. Comparative genomics between bacterial strains of varying biocontrol activities allow the identification of new candidate genes putatively involved in the biocontrol. This analysis will unravel novel insights into the biocontrol mechanisms of bacterial BCA and provide new resources for disease control [ 88 , 89 ].

Before bacterial strain is seriously considered for a microbial biopesticide development, pilot trials (greenhouse and field bioassays) must be conducted in several pathosystems and under diverse environmental conditions to ensure a wide range of applicability, as well as consistency in efficacy under real conditions [ 8 ]. Considering that the relative dose of pathogen and BCA is an important factor determining the efficacy and consistency of biological control, it is necessary to optimize the dose and frequency of applications. Dose–response models have been developed to obtain quantitative parameters that describe the efficacy of the BCA [ 90 ]. These parameters may give information on the dose range of the BCA needed to provide reliable, economical biological control, and allowing for the comparison of different BCA and pathosystems [ 8 , 69 , 90 ]. The required dose of BCA may be dependent on the mechanism by which a biocontrol agent performs its action. For a strain which acts via antibiosis or competitive exclusion it may be assumed that proper colonization is needed to deliver antimicrobial compounds or to compete with the pathogen, whereas for a strain which acts through ISR a smaller number of bacteria during a restricted period may be sufficient to elicit a successful response in the host plant [ 17 ].

3.3. Formulation and Delivery for Commercial Use

The final stages of B-BCA development include industrial scale production, formulation, and preservation. Suitable and cost-effective mass production at the industrial scale system must be carefully developed to obtain the highest number of cells in the shortest period. Moreover, it must be guaranteed that the production method does not alter the characteristics of the strains responsible for biocontrol. Culturing conditions determine population densities at the time of harvest and influence the viability and fitness of the microbes during formulation, storage, and application. These are however specific for each microbial strain and need to be screened carefully for improving final performance of microorganisms in the field [ 91 ]. Subsequently, developing an appropriate formulation (dry or liquid) is fundamental to increasing shelf-life, improving delivery, enhancing persistence in the field, and maintaining the viability and biocontrol efficacy [ 92 ]. Thus, the use of protective additives and adjuvants compatible with the BCA is common and they can be incorporated at different points of the production-formulation process. Classical protective substances (sucrose, glycerol, Arabic gum) improve survival of the microorganisms and adjuvants (surfactants, emulsifiers, dispersants, coupling agents, stabilizing agents) facilitate mixing, handling, application, and effectiveness [ 91 ].

In addition, biosafety studies must be undertaken to guarantee the lack of adverse effects of the active ingredient and the formulated product in plants and non-target organisms, including humans. It is also required to perform risk assessment studies on traceability, residue analysis, and environmental impact [ 8 ]. Thus, the development of reliable monitoring methods that accurately identify the released microorganism at strain level and track its population dynamics over time is a registration requirement [ 93 ]. Examples of strain specific quantitative monitoring methods developed for BCAs are real-time PCR for P. fluorescens EPS62e [ 94 , 95 , 96 ] or viable qPCR for L. plantarum PM411 [ 97 ]. These methods are useful for monitoring the fate and behavior of a released strain in the environment and for the quality control during production and formulation of the microbial biopesticide.

For placing the microbial biopesticide on the EU market, the active substance (i.e., bacterial BCA strain) needs to be approved at EU level and the formulated product must be authorized at Member State level (Regulation (EC) No 2009/1107 and (EC) 2017/1432). The registration procedure generally requires detailed dossiers accounting for scientific data on microorganism identity, biological properties, efficacy, specific analytical methods, residues, traceability, and potential adverse effects on human health and non-target organisms [ 8 , 93 ]. Microorganisms categorized as safe are highly appreciated for the development of microbial biopesticides. For example, bacteria designated with the GRAS and QPS status by the FDA and the EFSA, respectively, have a history of safe use in agriculture and in food and feed crops and lack known toxic or allergenic properties. These microorganisms are considered non-pathogenic to humans, or non-deleterious to the environment according. Therefore, the fact of belonging to this group facilitates the registration process for marketing.

4. Improvement of Biocontrol and Future Trends

The inconsistency in the performance of BCA in the biological control of phytopathogenic fungi and bacteria has limited their extensive use in commercial agriculture. Pathosystem factors such as host genotype, intrinsic characteristics of the pathogen, pathogen inoculum density, and environmental conditions have been shown to be key factors involved in the final levels of disease control achieved by bacteria. Multitude of biotic and abiotic factors can negatively influence the performance of the BCA, affecting their mechanisms of action or the multitrophic interaction between the plant, the pathogen, and the bacteria. However, some strategies can be adopted to improve the performance of BCA consisting of nutritional enhancement, physiological adaptation of BCA to stress and improvement of formulation ( Table 2 ), as well as genetic manipulation of microorganisms. In addition, another challenge is to develop specific delivery systems that favor the success of biocontrol programs. Delivery methods must be carefully selected based on the characteristics of a particular BCA against a specific pathogen. Bacteria can be applied directly to seeds by different methods such as biopriming, encapsulation, or fluid drilling, to soil by drenching, mixing, or microbigation, and on plant aerial parts by foliar spraying or directly into the vascular system by means of endotherapy [ 98 ].

Some strategies for the physiological improvement of bacterial biocontrol agents.

An improvement strategy of BCAs is based on nutritional enhancement, which consists of adding nutrients to the formulation that are more efficiently used by the biocontrol agent than by the pathogen. For example, the addition of glycine and Tween 80 to the formulation of P. fluorescens EPS62e improved its survival and adaptability in the plant environment [ 104 ] or the glucose analog, 2-deoxy-D-glucose enhanced biocontrol of blue mold on apples and pears [ 105 ]. Another effective approach to enhance the epiphytic establishment of BCA on plant surfaces is the physiological adaptation by osmoadaptation. This procedure based on the combination of saline osmotic stress and osmolyte amendment of the growth medium has been used to increase intracellular accumulation of osmolytes and drought stress tolerance. This strategy improved epiphytic survival and biocontrol efficacy of the apple blue mold biocontrol agent P. agglomerans EPS125 [ 101 ] and CPA-2 [ 106 ] and the fire blight biocontrol agents P. fluorescens EPS62e [ 102 , 103 , 104 ], P. agglomerans E325 [ 107 ] and L. plantarum PM411 [ 99 ].

The improvement of biocontrol can be achieved by application of mixtures of BCAs, the so-called consortia. This approach consists of designing mixtures of compatible strains that complement each other in terms of the mechanism of action and ecological attributes. This strategy may increase the efficacy and reliability of biocontrol in different environmental conditions, as well as provide a broader spectrum activity due to the synergistic effect of different mechanisms of action of the introduced biocontrol strains. Some examples are, dual mixtures of P. fluorescens and Pantoea sp. that enhanced the biocontrol of fire blight of pear [ 108 ], or mixtures of P. fluorescens producing different bioactive metabolites that improved the biocontrol of P. cactorum root rot in strawberry plants [ 30 ] and P. infestans in potato plants [ 109 ]. In some cases, the consortia include a high number of bacteria such in a consortium of seven different bacterial species used to protect maize against Fusarium [ 110 ] or a mixture of eight Pseudomonas strains that enhanced protection of tomato against bacterial wilt [ 111 ]. In addition, another possible strategy to improve the biocontrol efficacy is the amendment of BCAs with low toxic antimicrobial compounds. Several studies reported the combination with compounds such as bioregulators, organic acids, or essential oils. Improved biological control was reported by combining L. plantarum strains PM411 and TC92 with lactic acid [ 100 ], and Bacillus amyloliquefaciens or L. plantarum strains with essential oils [ 112 , 113 ]. Or in another approach, improved bioformulations containing living bacteria and concentrated culture supernatants with antimicrobial metabolites have also been reported [ 114 ]. Moreover, BCA performance can be improved by genetic alterations to enhance the efficacy of selected strains for biological control. This may be achieved by conventional approaches as well as through recombinant DNA techniques. However, regulation restrictions to apply and release genetically modified organisms (GMO) into the environment must be considered since genetic manipulation is an impediment for registration of a GM-biological control agent. Genetic engineered bacteria for development of improved bioformulations may offer a good opportunity for future. This approach may include engineered strains without foreign genes but containing useful mutations in genes affecting the biocontrol or strains containing genes from other bacteria. There are several examples of genetic improvement, such as the overproduction of the antimicrobial polyketides, pyoluteorin and 2,4-diacetylphloroglucinol, in P. fluorescens CHA0 [ 115 ] or the enhancement of mycosubtilin production in B. subtilis ATCC 6633 [ 116 ].

In conclusion, in recent years there have been important advances in the knowledge of BCA for the development of commercial products for bacterial and fungal disease management. However, large-scale implementation of biological control is hampered by the limitation of commercially available and efficient BCA. Future trends should include the identification of novel BCA and require rapid and robust screening methods suitable to evaluate high numbers of candidates. Moreover, a deep study of model BCA using comparative genome analysis, and genome, transcriptome and proteome analysis will provide a valuable framework allowing for a detailed analysis of the biological mechanisms of BCA and to design strategies enhancing its beneficial action. In addition, this multi-omics approach will allow to analyze the impact of field application of bacteria on the indigenous microbiome of plants. This study would allow analyzing the environmental impact of BCA, to ensure its biosafety, and understand how to modulate the microbiome to improve the efficacy of biocontrol.

Funding Statement

This research was funded by different grants from Spain Ministerio de Ciencia, Innovación y Universidades AGL2015-69876-C2-1-R, RTI2018–099410-B-C21, and from the European Union FP7-KBBE.2013.1.2-04 613678 DROPSA.

Author Contributions

Writing—original draft preparation, A.B., E.B., N.D., G.R., J.F. and E.M. Writing—review and editing, A.B., E.B., N.D., G.R., J.F. and E.M. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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General concepts of biological control.

July 2011 Diane Alston, Extension Entomologist (No longer at USU)

Definitions

Natural control.

“Balance of Nature.” Virtually all pest populations are affected by natural enemies to some extent. In many cases, natural enemies are the primary regulating force of the pest populations. Natural controls include effects of natural enemies (predators, parasites, pathogens), other biotic (living) factors such as food availability and competition, and abiotic (non-living) factors such as weather and soil.

Biological Control

“Any activity of one species that reduces the adverse effect of another.” In pest management, biological control usually refers to the action of parasites, predators or pathogens on a pest population which reduces its numbers below a level causing economic injury. Herbivorous insects and pathogens that attack pest weeds are also considered biocontrol agents.

Biological control is a part of natural control and can apply to any type of organism, pest or not, and regardless of whether the biocontrol agent occurs naturally, is introduced by humans, or manipulated in any way.

Biological control differs from chemical, cultural, and mechanical controls in that it requires maintenance of some level of food supply (e.g., pest) in order for the biocontrol agent to survive and flourish. Therefore, biological control alone is not a means by which to obtain pest eradication (Fig 1).

Important Characteristics

• Often relatively inexpensive and can be “permanent” for those biocontrol agents that can survive multiple years and become self-perpetuating.
• Effectiveness can be from low to high.
• Can be disrupted by other pest management tactics, especially broad-spectrum pesticides.
• Suppressive effects are density-dependent; it will have its greatest impact when pest densities are high.
• Often pest-specific, not broad-spectrum.
• Often a lag time between buildup of the pest population and buildup of the biocontrol agent (see Fig. 1); generally not fast-acting.
• Good tactic to include in a multi-tactic approach (IPM); fits in well with cultural, mechanical, and some chemical controls.
• Most successes have been in perennial crops (orchards, vineyards), rangeland, and field or forage crops which can withstand a moderate level of pest injury.

General Methods

Biological control agents can be purchased from commercial suppliers and released for supplementary control of pests. However, most biological control occurs without assistance from people. Many predators, parasites and pathogens occur naturally and are continually working to help keep nature in balance. The importance of natural enemies is often not appreciated until a broad spectrum pesticide, which kills many beneficials as well as the targeted pest, is applied and a new pest – suddenly released from biological control – becomes a serious problem. Conservation and enhancement of natural enemies already present in the system can be a very effective method of biological control.

1. Introduction = Importation

This is the “classical method” of using biological control. It has been used most for introduced or “exotic” pests. The origin of the pest is determined and then a search for natural enemies in its native habitat is conducted. Potential biocontrol agents are imported to the new location of the pest and released. Generally, the hope is for permanent establishment of the natural enemy.

Classic insect example: In 1888, importation of the Vedalia beetle (predaceous lady beetle) from Australia to California citrus groves for control of the Cottony cushion Scale. The scale is native to Australia

2. Augmentation = Mass Culture or Collection and Release

Inundative Release – a single release of large numbers of a natural enemy; release can be in a small or large area; natural enemy does not become established and reproduce; goal is a one-time reduction in pest numbers.

Inoculative Release – multiple, smaller releases of a natural enemy over a period of time; natural enemy is expected to colonize and spread in the area of release.

3. Conservation and Enhancement

Utilization of practices that protect, maintain and enhance already existing natural enemy populations. Such practices could include habitat diversification to provide additional shelter or food for a natural enemy, provision of artificial food supplements, use of pesticides that are selective for target pests and have minimal effects on natural enemies, avoiding cultural practices that disturb or destroy natural enemies, etc.

Agents of Biological Control

Adults feed individually, while larvae tend to destroy host plants in groups. Both stages are phytophagous, meaning they create holes within leaves or cause complete defoliation (Fig. 5). This may reduce plant vigor, photosynthesis, and yield in tomatillos. Like other leaf beetles, the three-lined potato beetle evolved a physiological pathway to avoid/tolerate the lethal tropane alkaloids found in various nightshade plants.

1. Parasites and Parasitoids

Parasite – an organism that lives in or on the body of another organism (the host) during some portion of its life cycle.

Parasitoid – an arthropod that parasitizes and kills another arthropod (insects, mites, spiders, and other close relatives) host; a parasitoid is parasitic in its immature stages and free living as an adult.

Parasitoids have been used in biological control more than any other type of agent. The major types of insects that are parasitoids: wasps, flies, some beetles, mantisflies, and twisted-winged parasites. 

Adult female parasitoids lay their eggs inside the host (the host arthropod is usually in its immature stage) by penetrating the body wall with their ovipositor or they attach their eggs to the outside of the host’s body.

2. Predators

Predator – “Free-living animal that feeds on other animals (prey); it may attack prey in both its immature and adult stages; usually more than one prey individual is required for the predator to complete its life cycle.”

Major types of animals that are predators: birds, fish, amphibians, reptiles, mammals, arthropods, and some plants (e.g., Venus fly trap). Major types of insects that are predaceous: dragonflies and damselflies, mantids, true bugs, some thrips, lacewings and relatives, beetles, some wasps and ants, and some flies. Spiders and some mites are also important predators of arthropods.

3. Pathogens

Use of microbial pathogens has become a very popular method of pest management. Major pathogens used in biological control of insects:

Bacteria – Bacillus thuringiensis = Bt (many caterpillar  pests, beetles, mosquitoes, others).

Viruses – Nucleopolyhedrosis viruses (Gypsy moth, European corn borer), granulosis viruses (Codling moth).

Fungi – Metarhizium (cockroach motels), Beauveria bassiana (Colorado potato beetle, Corn rootworms).

Protozoa – Nosema locustae (grasshoppers).

Nematodes – Steinernema and Heterorhabditis spp. (Soil weevils, Stem-boring caterpillars).

4. Herbivorous Insects and Microbial Pathogens of Weed Pests

Numerous species of plant-feeding insects have been evaluated for control of pest weeds. The greatest successes have been in rangelands, forests, and other natural habitats where other weed control approaches (e.g., herbicides, cultivation) are impractical or uneconomical. Some pathogens have also been looked at as weed biocontrol agents (e.g., plant rusts). The goal when using a weed biocontrol agent is generally one of weed population reduction and not eradication. Importation of a biocontrol agent from the region of origin of the weed has been the most common approach. It is generally a long-term process which requires sustained efforts, but which can reap long-term benefits.

Some classic examples:

• Importation of a moth to control prickly pear in Australia; the larvae bore into the stalk of the cactus allowing entry of secondary disease organisms.
• Introduction of a leaf-feeding beetle to control Klamath weed in the western U.S.

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Proactive resistance management for sustaining the efficacy of RNA interference for pest control

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Yulin Gao, Andrei Alyokhin, Runzhi Zhang, Guy Smagghe, Subba Reddy Palli, Juan Luis Jurat-Fuentes, Bruce E Tabashnik, Proactive resistance management for sustaining the efficacy of RNA interference for pest control, Journal of Economic Entomology , 2024;, toae099, https://doi.org/10.1093/jee/toae099

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Biopesticides based on RNA interference (RNAi) took a major step forward with the first registration of a sprayable RNAi product, which targets the world’s most damaging potato pest. Proactive resistance management is needed to delay the evolution of resistance by pests and sustain the efficacy of RNAi biopesticides.

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Improving wasp control by identifying likely causes of eradication failure

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• M. W. F. Howse   ORCID: orcid.org/0000-0001-8070-3379 1 ,
• A. Reason   ORCID: orcid.org/0000-0001-6448-7047 1 ,
• J. Haywood   ORCID: orcid.org/0009-0006-9414-2801 2 &
• P. J. Lester   ORCID: orcid.org/0000-0002-1801-5687 1  

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Studying the efficacy of control methods is paramount to successful management of invasive pests and understanding why some colonies survive is important to improve management practices. Here, the bait Vespex® was used to control invasive wasps across 64 ha of forest in an invaded range near Hanmer Springs, New Zealand. Bait was applied across a standard 50 m by 300 m arrangement and made available for 3 days. Nest mortality rates after 19 days were 29.8%, although nearly all nests were affected with a median overall reduction in nest traffic of 96.5%. The results from logistic regression showed that, all else remaining equal, larger wasp nests, nests further from bait stations, and more isolated nests exhibited lower rates of mortality after baiting. Investigating the change in activity at surviving nests, the results from beta regression suggest that declines in nest traffic were less severe with increasing distance to the nearest bait, but more severe with increasing nest size. These results indicate that while smaller nests are at a higher risk of being killed by the bait, they may not encounter bait as regularly as larger nests. Bait uptake varied considerably across bait stations. Wasp nests were not randomly or uniformly distributed in space, and instead were aggregated across our treatment area, likely due to some aspects of environmental conditions. We suggest further research to be focused on developing an understanding of the drivers of wasp nest development and foraging behaviour, to produce a more flexible baiting procedure that will increase both baiting efficiency and efficacy.

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Introduction

Globalisation has increased the spread of invasive species to new ranges with significant impacts on local ecosystems (Bellard et al. 2016 ). To combat these effects conservation managers have initiated many attempts at population suppression or outright eradication with varying degrees of success (Pluess et al. 2012 ; Tobin et al. 2014 ). Social insects are overrepresented in biological invasions and have been shown to be especially difficult to remove once established (Howse et al. 2023 ). Understanding why attempts at population suppression and eradication fail can improve future management of these invasive species.

Social wasps are an example of a globally significant invader (Lester and Beggs 2019 ). A flexible reproductive strategy and high output has allowed social wasps to reach high densities in new ranges all over the world (Beggs et al. 2011 ; Hanna et al. 2014 ; Lester and Beggs 2019 ; Loope and Wilson Rankin 2021 ). Their generalist feeding behaviours and habitat preferences allow them to readily dominate available resources (D'Adamo and Lozada 2009 ; Lester and Beggs 2019 ; Howse et al. 2020 ; McGruddy et al. 2021a ; Moreyra and Lozada 2021 ) altering both recipient animal and plant communities (Thomas et al. 1990 ; Beggs and Wilson 1991 ; Beggs and Rees 1999 ; Beggs 2001 ; McGruddy et al. 2021b ). Invasive wasps also represent a very important threat to apicultural industries as they act as both a competitor and direct predator of honey bees as well as a reservoir for honey bee viruses (Brenton-Rule et al. 2018 ; Dobelmann et al. 2020 ; Buteler et al. 2021 ). Wasps were implicated in as much as 14.9% of overwintering hive losses in New Zealand per year since 2015 (Stahlmann-Brown et al. 2022 ). A study by MacIntyre and Hellstrom ( 2015 ) estimated that invasive Vespula wasps cost the New Zealand economy NZ$133 million per year for their impacts on agriculture and human health.

Reducing the population of these invasive wasp species has become a priority for conservation managers globally (Edwards et al. 2017 ; Lester and Beggs 2019 ; Wilson Rankin 2021 ). Nest destruction, trapping and biological control have all been employed to control populations of invasive wasps but with limited success, especially at large scales (Beggs et al. 2011 ). Currently the most effective methods for controlling invasive wasps are toxic baits. These baits contain an insecticide within an attractive matrix. The wasps collect the bait and pesticide, taking it back to the nest where it is spread to other colony members via trophallaxis (Beggs et al. 2011 ). The commercially available bait, Vespex® (Merchento, New Zealand) contains the active ingredient fipronil (0.1%) which is a neurotoxin and broad-spectrum insecticide, within a matrix that attracts foraging workers. This bait is typical of many wasp control approaches and is designed to target vespuline wasps, relying on the species’ scavenging behaviours to take the protein-based bait back to the nest to be distributed to the other nest members. Vespex® has been shown to be effective at controlling wasps at scales of over 2000 ha, reducing nest activity by over 97%, 20–38 days after baiting (Edwards et al. 2017 ). Similar fipronil-based baits have been shown to significantly reduce wasp activity across the globe (Sackmann et al. 2001 ; Hanna et al. 2012 ; Rust et al. 2017 ) but eradications at landscape scales have not been achieved.

Previous studies examining the impact of toxic baiting efficacy have measured wasp activity by calculating nest traffic rates (Sackmann et al. 2001 ; Hanna et al. 2012 ; Edwards et al. 2017 ). Monitoring changes in nest traffic provides a measure of changes in foraging pressure exerted on an ecosystem. Nest traffic rates also provide information on colony size (Malham et al. 1991 ). Colony size in social insects is linked to important processes related to survival such as reproduction (Melo et al. 2023 ), colony defence (London and Jeanne 2003 ) and nest hygiene (Maák et al. 2019 ) and has been associated with increased genetic diversity and reduced viral load (Dobelmann et al. 2017 ), which may alter susceptibility to toxic baits. Factors that govern bait discovery and uptake are also important to understand when addressing bait efficacy. Nest-to-bait distance has previously been shown to impact baiting efficacy (Harper et al. 2016a ). Investigating the arrangement of nests relative to both baits and to each other may provide insights into opportunities for increasing baiting efficacy.

In this study, a current wasp control method using the fipronil-based bait, Vespex®, was used to highlight what factors are most associated with wasp nest survival during a baiting operation. Our goal was to identify where measures might be improved for more effective wasp suppression in the pursuit of higher levels of population control or even eventual eradication at large scales. The methods were implemented using the manufacturer’s recommended protocol, informed also by Edwards et al. ( 2017 ), to control the common wasp ( Vespula vulgaris ) in a beech forest habitat, in New Zealand. Logistic and beta regression were used to analyse how aspects such as nest size and spatial distribution of both baits and nests influenced the magnitude of the bait’s effects.

The experiment took place on a privately-owned site in Northern Canterbury, New Zealand (Fig.  1 ). The focal site of the experiment was a 64-ha area within a patch of remnant beech forest with a canopy of predominately mountain beech ( Fuscospora cliffortioides ) but also including pockets of tōtara ( Podocarpus totara ) and other native species. The understorey was primarily beech saplings F. cliffortioides as well as mingimingi ( Coprosma propinqua ). The patch of forest containing the focal experiment site is located on the southeast side of Mt Balfour (42.4679° S, 173.0107° E) ranging from 636 to 878 m in elevation. A second site used as an untreated reference location was approximately 4 km south from the treatment site (41.3987° S, 172.9908° E). This reference site was used to account for natural seasonal variation in wasp activity. The reference site was in a valley on the eastern side of the Hanmer River. The northern side of the valley was covered in a similar forest habitat as in the treatment area while the southern side contained more altered open habitat. The area searched here was approximately 16 ha in area and varied from 554 to 700 m in elevation.

Maps showing the position and arrangement of the treatment and reference sites. Panel a shows a map of New Zealand with the study location marked in red. Panel b shows where the treatment and reference sites were relative to each other with the point marked ‘T’ representing treatments site and point marked ‘R’ representing the reference site. Panels c and d show maps of the Treatment and Reference sites, respectively. Red triangles show the position of all wasp nests discovered prior to baiting while blue squares mark the position of bait stations. Baits were spaced at 50-m intervals with 300-m spaces between baitlines as per manufacturer guidelines. The distance between these sites is approximately 4.5 km. Maps made in QGIS version 3.16 (QGIS Development Team 2021 ) using base map data sourced from Land Information New Zealand ( https://data.linz.govt.nz/ )

Wasp nest marking and monitoring

In January 2023 over the course of 10 days (10–20 January), both the 64-ha treatment area and the 16-ha reference area were systematically searched for wasp nests. The areas were visually surveyed by two researchers walking slowly through the forest block. When a nest was discovered, it was marked with flagging tape and the location and elevation recorded using a GPS (Garmin 65 s) accurate to 15 m. The traffic rate, or average number of wasps entering and exiting the nest over the course of 1 min, was recorded for each nest. The traffic rate of the nest is an established measurement of wasp activity and is linked to colony size (Malham et al. 1991 ). One researcher counted the number of wasps entering the nest while, at the same time, another researcher counted the number of wasps exiting the nest. The sum of these numbers was recorded as a traffic rate over 1 min and the process was repeated 3 times sequentially, to calculate an average traffic rate. All traffic rate monitoring occurred during daylight hours between 9:00 am and 5:00 pm. Monitoring was halted during rain.

In March 2023 (20–22 March), these nests were resurveyed prior to baiting. Earlier re-surveying and baiting was thwarted by extreme weather events in February which made the sites inaccessible until mid-March. Traffic rates and any mortalities were measured and recorded before any bait was applied. Additionally, 17 nests that were discovered had their traffic rates calculated and recorded. A nest was considered dead if no wasps were observed during the 3 min of observation which was usually confirmed with another 3-min observation period the following day. Given the life history of these wasps, we assumed that all nests found in March, prior to baiting, were alive in January and were used to estimate natural or pre-baiting mortality rates. Baiting commenced on 23 March 2023 (for the purpose of the analysis this date is considered day 0).

Baiting regime

Within the 64-ha plot of forest, 4 bait lines were established. Wasptek™ bait stations (Merchento, New Zealand) were screwed onto trees at approximately 1.2 m high. These bait stations were spaced at 50-m intervals along the line with each line being spaced 300 m apart as suggested by the manufacturer and as used by Edwards et al. ( 2017 ). These distances were measured using GPS. In total 52 bait stations were used across the 64-ha treatment area (Fig.  1 ). In each bait station, approximately 30 g of Vespex® bait (chicken-based paste with 1 g/kg fipronil) was applied on 23 March 2023. On 26 March 2023, any remaining bait was collected and removed from the site as per manufacturer and Environmental Protection Authority (EPA) guidelines. The duration we allowed the bait to remain in the environment was the minimum suggested by the manufacturer. It is recommended to bait during sunny, clear conditions to encourage the greatest bait uptake. As mentioned, extreme weather events forced the baiting operation to take place later in the season than previously planned and so there were constraints on time due to site accessibility and appropriate baiting weather. The baiting window was flanked by periods of wind and precipitation and so could not be extended. Due to this constraint the bait was left out only 3 days compared to other studies such as Edwards et al. ( 2017 ) who had bait available for between 7 and 13 days.

Post-bait monitoring

Nests were checked and monitored again immediately after baiting. Due to the size of the field site, it took two days to check all nests across both reference and treatment sites. For this analysis, counts were clustered together and ascribed the later assessment date. All nests were checked by 27 March, the 5th day after baiting (hereafter, day 4). Traffic rates were calculated as described above, and any nest mortalities were recorded. Finally, nests were checked again in April, with all nests monitored by 11 April, the 20th day after baiting (hereafter, day 19). Similar fipronil-based baits have been shown to produce declines in nest traffic rate 24 h after application (Sackmann et al. 2001 ).

Nest extractions

Once all data collection had taken place, 15 of the surviving nests were exhumed for observation to visually assess the health of the surviving nests and assess the severity of sublethal effects of the bait. Twelve nests in the treatment site and three from the reference site were excavated and examined. Excavations occurred between 21 and 22 days after bait application. The size of the nest was visually inspected and evidence of new season queens as well as dead or dying workers was noted. These observations were purely qualitative, so no formal analysis was carried out.

Bait consumption

Thirteen bait stations were concurrently monitored to assess the level of bait uptake by wasps. Identifying rates of bait uptake can indicate how much bait may be required for an area or how long bait can be left out. Looking at the variation in bait uptake may tell us where efforts could be focussed. Baits were weighed using digital scales when they were placed out in bait stations on 23 March and reweighed daily until they were brought in on 26 March 2023. To account for potential evaporation, 3 baits were placed in a ventilated cabinet that roughly imitated the forest conditions but prevented wasps or other species from accessing the bait.

Camera data

During the baiting process, cameras were placed out at a subset of nests to assess the daily activity patterns of wasps at the nest. Cameras were set to record 1 minute of video footage every hour between the hours of 06:00 and 22:00. Cameras were placed in front of six nests, three at the treatment site and another three in the reference site. A4-sized pieces of fluted plastic board were placed near the entrance of the nest to allow a better view of wasps entering and exiting the nest, as the plastic gave a more visually contrasting background (Fig.  2 ). Recording began two days before baiting commenced and ran for a further four days after baits were deployed. Cameras were placed out again in April at the same nests for a further day. Footage was analysed and traffic rates calculated for each time stamp across the filming period. Unfortunately, only two cameras managed to capture footage over this entire period, one in the treatment site and one at the reference site, so this data is presented as an example of activity patterns from two nests.

The remote camera set up. The camera (to the right) was powered by an external power pack. The nest entrance is indicated by the red arrow. The yellow plastic flute board was used to create contrast, to more easily observe wasps entering and exiting the nest

Statistical analysis

All statistical analysis was performed using R version 4.2.0 (R Core Team 2022 ). Mean proportional changes in traffic rate measured 19 days after baiting across the two sites were analysed using a Wilcoxon rank sum test with a continuity correction using the ‘wilcoxon.test’ function, from the ‘stats’ package. The test statistic ( W ) and p -value ( p ) are reported.

For the mortality analysis, logistic regression was used to evaluate how different variables influenced mortality. A binomial response variable was assigned to represent nest mortality (1 = alive, 0 = dead). Nest traffic rate has been shown to be a predictor of wasp nest size (Malham et al. 1991 ) so rates measured immediately prior to baiting were used as an estimate of nest size and denoted Size in the model. The distance between the nest and the nearest bait station in metres was also included as a term Bait distance . The terms Bait density 50 m, Bait density 100 m , and Bait density 200 m were used to denote the number of baits within a given radius of a wasp nest. Nest density 50 m , Nest density 100 m , and Nest density 200 m were three terms used to denote the number of other nests discovered within a given radius of a wasp nest. The full complement of terms was used to build an initial model using the ‘ glm’ function, from the ‘stats’ package in R studio (R Core Team 2022 ). Backwards stepwise regression was used to select the variables in the final model using the ‘step’ function in the ‘stats’ package (R Core Team 2022 ). This process starts with the full complement of terms in a generalised linear model and works backwards, removing terms to produce the greatest decrease in the Akaike’s Information Criterion (AIC). When the removal of any further variables would produce an increase in AIC the process stops, and we are left with the final variables selected. The ‘Anova’ function from the package ‘car’ (Fox and Weisberg 2019 ) was used to produce a Type II analysis of deviance table to assess the relationship between the response and explanatory variables in the selected logistic regression model. The likelihood-ratio chi square statistic ( G 2 ), degrees of freedom ( df ) and associated p -values ( p ) are reported. Statistical significance was assumed at p  < 0.05.

To explore potential drivers of sublethal effects, a subset of data was analysed using beta regression. Only nests that survived the baiting were used here. The proportional change in traffic rate was calculated from the traffic rate measured 19 days after baiting, divided by the traffic rate measured immediately prior to baiting for each nest. This proportion was used as the response variable Proportional change. A beta regression model was fitted using the ‘betareg’ function from the package ‘betareg’ (Cribari-Neto and Zeileis 2010 ). The model had the same full complement of terms listed above and similarly backwards stepwise regression was used to select terms for the final model using the ‘StepBeta’ function in the ‘betareg’ package. This selection process also used AIC to determine variable removal. A Type II analysis of deviance table was again produced using the ‘Anova’ function from the ‘car’ package (Fox and Weisberg 2019 ), producing likelihood-ratio chi square statistic ( G 2 ), degrees of freedom ( df ) and associated p -values ( p ).

To investigate how the degree of bait consumption impacts changes in wasp activity, the mean proportional change in nest traffic of nests within 50 m of the bait stations was plotted against the proportional change in bait mass at each station. To ensure non-negative fitted values the association was modelled using an exponential (semi-log) regression model in R (R Core Team 2022 ). A small positive constant (0.0001) was added to the mean proportional change in nest traffic, to avoid problems with logarithmic transformation of a response variable taking zero values. Note that as nest traffic could increase if any nests grew, no upper limit on proportional change in nest traffic exists in practice. The effect size and associated standard error, F -statistic ( F ), degrees of freedom ( df ), R 2 value and associated p -value ( p ) are reported.

Seasonal autoregressive integrated moving average (ARIMA) models (Hyndman and Athanasopoulos 2021 ) were used to investigate the effect baiting and temperature had on the nest traffic daily patterns recorded by the trail cameras. Regularly spaced hourly traffic data from each nest were converted into a time series. Hourly temperature readings from the nearest weather station were retrieved from the CliFlo database ( https://www.cliflo.niwa.co.nz/ ). An indicator variable was included to represent times following bait deployment (Online resource 1 ). ARIMA models were fitted to the observed nest traffic using the ‘auto.arima’ function in the R ‘forecast’ package (Hyndman and Khandakar 2008 ), and a best model was selected by minimising Akaike’s Information Criterion (AIC).

We analysed the spatial arrangement of wasp nests found in the treatment site to compare to the arrangement of baits using nearest neighbour index (NNI). These statistics were calculated using the ‘spatstat’ package (Baddeley and Turner 2005 ) in R. GPS points of nests were converted to point pattern datasets using the ‘ppp’ function in the ‘spatstat’ package. A polygonal boundary was drawn around the outermost GPS points to form the observation window. The NNI was then calculated as the observed mean nearest neighbour distance divided by the expected mean nearest neighbour distance (Clark and Evans 1954 ; Rogerson 2001 ). This expected value is derived from a hypothetical point pattern with the same number of points following a random distribution across the same sized area. A value less than one suggests an aggregated distribution and a value over 1 suggests a dispersed or uniform distribution. The observed mean nearest neighbour distance was calculated using the ‘nndist’ function. The expected mean nearest neighbour distance ( \({\overline{r} }_{{\text{Expected}}}\) ) was defined as one-half times the square root of the number of points ( N ) divided by the area of the observation window ( A ) (Clark and Evans 1954 ).

A z -test was conducted, and significance was assumed at p  < 0.05.

In total 75 wasp nests were discovered and monitored prior to baiting. Fifty-nine wasp nests were discovered during the January monitoring period, 50 nests in the treatment site and 9 in the reference site. In March, a further 16 nests were discovered (13 in the treatment site, 3 in the reference site). All but 3 nests were confirmed to be Vespula vulgaris. These other nests belonged to the related Vespula germanica and were excluded from the data and analysis.

Between January and March, prior to baiting, 3 out of 72 nests died. All 3 nests were from the treatment site, but no wasp control action had taken place over this time and so these mortalities could only be attributed to natural events such as flooding (a number of nests were in close proximity to waterways).

Across all nests in the treatment site, the bait application produced a median reduction in traffic rate of 96.5% by day 19 (Fig.  3 a). By comparison, traffic rates in the reference site were more variable with a median decline of 38%, 19 days after baiting. The mean proportional change in traffic rate was significantly larger at the treatment site than at the reference site ( W  = 649, p -value < 0.001). Over the course of the study, no nests in the reference site were observed to die. In the treatment site one nest had failed 4 days after baiting. By day 19, the failed number was 17 out of 57 or 29.8% (Fig.  3 b).

Plot a shows a pair of boxplots comparing the change in traffic rates measured before and after baiting at both the treatment and reference sites. The y-axis shows the proportion of the traffic rate measured prior to baiting that remained 19 days after baiting. The dashed line represents a value of 1 and values over or under 1 represent an increase or decrease in traffic rate, respectively. A value of zero indicates a nest that has died. The median value for nests in the reference site was 0.62, suggesting a drop in traffic rates of 38%. The box indicates the interquartile range while the bold black line shows the median value. The median decline in traffic rates in the treatment site was much more severe at 96.5%. Plot b shows binned traffic rate declines at nests in the treatment area. The numbers above the bars show the number of nests within each bin. All nests within the treatment area experienced declines in traffic rate after 19 days. Seventeen nests out of 57 experienced declines of 100% meaning that no wasp activity was observed at these nests during post-bait monitoring, so they were considered dead

Logistic regression was used to investigate predictors of wasp nest mortality, with variables for the generalised linear model selected via backward stepwise regression. The variables included in the final model were Size, Bait distance, Nest density 50 m and Nest density 100 m . The size of the nest was found to have a positive association with nest survival suggesting that, given all else, larger wasp nests were more likely to survive baiting than smaller ones ( β Size  = 0.040 ± 0.020, G 2  = 5.317, df  = 1, p  = 0.021) (Fig.  4 a). A positive association was also found between mortality and Bait distance suggesting that, given all else, the odds of survival increased at nests with increasing distance to the nearest bait ( β Bait distance  = 0.025 ± 0.010, G 2  = 8.519, df  = 1, p  = 0.004) (Fig.  4 b). Interestingly, the analysis showed a significant positive association between mortality and nest density. The term Nest density 50 m had a significant negative coefficient ( β Nest density 50 m  = − 1.133 ± 0.392, G 2  = 12.179, df  = 1, p  = 0.001) indicating that, given a constant value at all other variables, an increase in the number of other nests within 50 m of a given nest leads to a higher odds of nest mortality. The term Nest Density 100 m had a significant positive coefficient ( β Nest density 100 m  = 0.467 ± 0.232, G 2  = 5.720, df  = 1, p  = 0.017).

Bivariate plots of lethal and sublethal effects of toxic baiting. On plots a and b the y-axis shows nest survival, where a value of 1 or 0 represents a nest that survived or did not survive baiting, respectively. In plot a, traffic rate count prior to baiting was used as a proxy for nest size and was shown to have a positive association with nest survival. This trend suggests that smaller nests experienced higher chances of mortality. Plot b shows that the distance from the nest to the nearest bait is positively associated with nest survival, suggesting that nests closest to baits experienced higher chances of mortality. Plots a and b show fitted sigmoidal trendlines. Plots c and d include only data from nests that survived baiting. The y -axis of plots c and d shows the proportion of the traffic rate measured prior to baiting that remained 19 days after baiting. A value of 1 represents no change in traffic rate while values closer to 0 represent a larger decrease in traffic rate and therefore more significant sublethal effects. Plot c shows that with increasing nest size (measured as traffic rate prior to baiting), nests tended to experience greater declines in nest activity. Plot d shows that with increasing distances from the nest to the nearest bait, declines in nest activity reduce. Blue lines in plots c and d show fitted exponential trendlines

Sublethal effects

Nests that survived baiting at day 19 often still exhibited important sublethal effects, most notably a decrease in traffic rate. Across the 40 surviving treatment nests, by day 19 traffic rates had declined by 83% on average, with all treatment nests experiencing a reduction in traffic rate by this time. Nests in the reference site, on average, also experienced an overall decline in traffic rate of 30% by day 19, but trends here were more varied with some nests increasing in activity. Cooler conditions almost one month later may have contributed to this decline.

Beta regression analysis was used to investigate the predictors of the severity of sublethal effects in the nests that survived baiting. In this analysis, nests that died were excluded. The proportional response variable Traffic rate proportion was calculated as the final traffic rate count divided by the traffic rate count immediately prior to baiting. Variable selection was carried out using AIC and backward stepwise regression. The predictor variables used in the final model were Size, Bait distance and Nest density 100 m. Based on this subset of nests, Size appeared to have a significant negative association with Traffic rate proportion ( β Size  = − 0.018 ± 0.006, G 2  = 8.331, df  = 1, p  = 0.004) (Fig.  4 c). This negative association implies that the larger of the remaining living nests, given all else, would be expected to experience a more dramatic decline in traffic rate. Bait distance was found to have a positive association with Traffic rate proportion ( β Bait distance  = 0.009 ± 0.003, G 2  = 6.597, df  = 1, p  = 0.010) (Fig.  4 d). Like mortality, this association would suggest that given all else remains constant, nests further from bait stations would expect to experience less severe declines in traffic rate after baiting. Nest density 100 m was also included in the model using the AIC criterion and showed a negative association with Traffic rate proportion ; however, this was found not to be statistically significant ( β Nest density 100 m  = − 0.090 ± 0.050, G 2  = 3.231, df  = 1, p  = 0.072).

Nests in the treatment site reacted less aggressively to disturbance and extraction of the nest, with few workers typically observed defending the nest. Evidence of new season queens was present at all nests, suggesting the baiting did not achieve the ‘reproductive failure’ of the treatment nests sampled. Nests extracted in the treatment site also contained a large number of dead wasps at varying states of decay (Fig.  5 ). A strong, sour odour was emitted as we broke into the bottom layers of these treatment nests where dead workers appeared to accumulate. Mould was growing among the bodies of these wasps. Such accumulations were absent from the three nests excavated from the untreated reference site. Nests in the treatment site were smaller than those excavated from the reference site with, on average, 9.7 layers compared to 14.3 layers, respectively.

Pictures of a nest that was extracted from the treatment site in mid April 2023. Inside this nest, and in others from the treatment site, were large numbers of dead wasps that appeared to be decaying within the nest after baiting. There was a strong odour detected once the outer nest envelope was removed and large numbers of dead wasps were found mainly among the lower layers of the nest. These large deposits of dead wasps were not present in the nests extracted from the untreated reference site

Some wasps were observed to be consuming bait within an hour of it being placed in the bait stations, however, all stations contained bait remaining when checked on day 3 after baiting. Bait stations checked daily during the operation had dropped on average 3.26 g per day. The reference baits had lost an average of 1.62 g per day suggesting that, accounting for evaporation, an average of 1.64 g of bait per day was consumed at each station over the course of the baiting period. After 19 days, reference baits had decreased in mass by 16.7% on average, while baits out in the treatment site experienced a decrease of 33.1% on average (Fig.  6 a). Bait consumption was variable across different stations in the treatment site. Bait station 8, located in a shaded, heavily forested valley, experienced less change in mass than even the three reference baits at 14.8%, while bait station 13, located on the bush edge lost 54.2% of its initial mass, by day 3 (Fig.  6 a). This suggests bait placement in the environment may influence bait uptake. To investigate whether bait consumption was associated with changes in wasp activity, the proportion of bait remaining after the conclusion of the operations was plotted against the mean proportional change in traffic rate after 19 days, using nests within 50 m of each bait station (Fig.  6 b). There was a statistically significant positive association ( β local nest decline  = 18.02 ± 6.59, F  = 7.475 on 1 and 8 df , R 2  = 0.483, p  = 0.026).

Plot a shows the proportion of bait remaining at the 13 selected stations compared to 3 reference baits. Treatment bait stations (blue lines) were placed out on 23 March (day 0) and weighed daily until they were retrieved on 26 March (day 3). To account for evaporation, 3 reference baits (red lines) were placed in a ventilated cabinet to simulate outdoor forest conditions but prevent wasps from accessing the bait. Plot b shows the association between bait consumption and nest activity decline. Proportion of bait remaining is plotted on the x -axis. The y -axis shows the mean of the proportion of pre-baiting traffic rate calculated as the traffic rate of local nests measured 19 days after bait application divided by the traffic rate of those same nests measured prior to baiting. Only nests within 50 m of the bait stations were used in this analysis. The numbers shown on the plot correspond to the bait station. Some bait stations are excluded from the plot due to the lack of wasp nests within 50 m of the bait. The dark blue line of best fit shows a positive and statistically significant association between variables

Daily activity patterns

Camera footage captured at the entrance of two wasp nests allowed observation of wasp activity patterns (Fig.  7 ). The observed nests tended to be active for between 13 and 14 h per day. Wasp activity began between 6:00 and 7:00 and ceased between 20:00 and 21:00, approximating to roughly 1 h before sunrise and 1 h after sunset, respectively. Nest activity was relatively variable and likely dependent on local weather conditions. Conditions on 21, 22 and to a lesser extent 23 March were comparatively cooler and wetter than the rest of the week (24–26 March 2023) and may have resulted in the slightly lower traffic rates seen in Fig.  7 . Often two main spikes in activity occurred in the late morning and afternoon. The treatment nest shows a decline in traffic rate after baiting, which would be expected if the bait was impacting the nest. The reference nest from the untreated reference site does not show the same declining pattern, instead traffic rate increases in the 3 days after baiting. For both nests, various seasonal ARIMA models were constructed with a seasonal period of 24 h, using the regularly spaced observations on nest activity as the response variable, with temperature and bait presence as possible predictors. Neither temperature nor bait presence could significantly improve the model fit when evaluated by AIC. The time series of nest activity in the treatment site was best explained by a seasonal ARIMA(1,0,0)(1,1,0) 24 model, which used activity patterns from the previous day as an important predictor and also included a negative drift term to explain the long-term, hour-to-hour decline observed in mean nest activity (Online resource 2). The time series of nest activity in the reference site was best explained by a seasonal ARIMA(1,1,1)(0,1,1) 24 model, which again used activity patterns from the previous day as an important predictor. The reference site model also used the activity from the previous hour as a predictor and hence did not need a drift term to explain long-term patterns within the data (Online resource 2 ).

Plot of wasp nest traffic at two nests captured using automated camera traps. Nest traffic rates were counted using the footage captured at 1-h intervals from 6:00 to 22:00 daily. Filming began on 21 March 2023. Baiting occurred on 23 March, indicated by the dashed line and blue shading. Cameras were retrieved on 26 March, providing 3 full days of footage after baiting. Cameras were placed out again at the same nests on 11 April, providing footage from 19 days after baiting, although this latter data was not fitted using the time series models. Red squares show the activity measured at the nest in the reference site while blue circles show the activity patterns at the nest in the treatment site

Spatial analysis of nests

Visual assessment of the spatial arrangement of both the discovered wasp nests and bait stations within the treatment site suggest that the two follow different patterns (Fig.  1 c). While the bait stations were systematically deployed in space, wasp nests identified and monitored over the course of the baiting program were found to be spatially aggregated. The observed mean nearest neighbour distance was calculated to be 36.9 m. Based on the Clark and Evans ( 1954 ) formula, the expected nearest neighbour distance for our searched area was 178.5 m producing a nearest neighbour index (NNI) value of 0.209 ( z  = − 3.394, p  = 0.001). This value is less than 1, indicating a significantly aggregated or clustered distribution.

The aim of this research was to identify the factors that might be responsible for the survival of wasp nests during population control using toxic baits. The fipronil based bait, Vespex®, was shown to produce a knockdown in wasp numbers and killed many nests. A median decline in traffic rate of 96.5% across all treated nests was observed with a mortality rate of 29.8%, 19 days after bait application. All nests in the treatment area experienced a decline in traffic rate. Nests at the reference site showed a more variable pattern with an overall median decrease in activity of 38%. Some nests at the reference site showed declines in activity while for others activity increased over time. Logistic regression analysis suggested that all else remaining equal, larger nests, nests further from baits, and nests more isolated from their neighbours appeared to be associated with higher odds of survival after baiting. Similarly, beta regression suggested that given all else remaining equal, less severe sublethal effects of baiting were associated with smaller nests and those nests further from bait stations.

Some of these associations may be explained by patterns of forager recruitment to bait stations. Recruitment is the process by which individuals can signal nestmates to gather at a location, usually to exploit a resource (Czaczkes 2021 ). Social wasps including Vespula spp. exhibit some level of recruitment behaviours where foragers exchange information to promote nestmate discovery of the same resources (Schueller et al. 2010 ; Wilson-Rankin 2014 ; Santoro et al. 2015 ; Lozada et al. 2016 ). When competition for food resources is high wasps have been shown to quickly capitalise on resources close to the nest (Wilson-Rankin 2014 ), potentially explaining why we observed that both nest mortality and severity of sublethal effects are associated with distance to nearest bait. A link between recruitment ability and nest size was shown by Wilson-Rankin ( 2014 ), where smaller annual nests exhibited higher bait visitation in response to successful nestmates returning, compared to larger perennial nests. The author goes on to hypothesise that the wasps from the larger perennial nests may be used to foraging at greater distances from the nest and may overshoot baits that other nestmates have discovered. This foraging pattern may suggest that if a bait is discovered, small nests will more readily exploit it leading to larger doses of insecticide and increased chance of mortality. Larger nests may, in turn, show a weaker response to these baits which could explain their increased survival.

Larger nests have both physiological and behavioural advantages that may explain their lower risk of mortality compared to smaller nests. Dobelmann et al. ( 2017 ) showed that larger V. vulgaris nests often contain higher genetic diversity and lower viral loads. Higher genetic diversity and lower viral loads have subsequently been linked to increased insecticide tolerance in social insects (Milone et al. 2020 ; Zhu et al. 2022 ). Additionally, it was shown that insecticide tolerance increased with group size in termites, likely due to an increase in stress relieving behaviours (DeSouza et al. 2001 ; Watanabe et al. 2023 ). These benefits may explain increased survival of our larger nests, in addition to larger nests needing higher doses of toxic bait to achieve mortality. In the context of population control, it seems likely that ensuring enough bait is taken up by wasps from larger nests is essential to produce nest failure. Allowing for longer baiting windows may facilitate higher bait uptake.

The apparent susceptibility of larger nests to sublethal effects is likely a case of survivorship bias. The beta regression identified a negative association between nest size (measured as the traffic rate count of nests prior to baiting) and the severity of sublethal effects (measured as the proportion of the traffic rate measured prior to baiting that remained 19 days after baiting). It is possible that small nests that survived baiting experienced reduced sublethal effects because they did not encounter bait. Larger nests rapidly deplete resources near to them and so tend to have to forage further from their nest than their smaller counterparts (Wilson-Rankin 2014 ). This increased foraging distance makes it more likely that foragers from larger nests encounter baits in the landscape. Smaller nests by comparison have a reduced need to forage as widely and are less likely to encounter baits further from them. Increasing bait density would be needed to ensure that these smaller nests encounter baits and perhaps increase the efficacy of this eradication treatment.

Our analysis showed that in scenarios where all else would remain constant, nests closer to baits would experience more significant declines in traffic rate while those further away would be less affected. This result is mirrored in a study by (Harper et al. 2016b ) who similarly showed that nests located further from bait stations experienced less severe declines in traffic rate. Despite this trend, both the Harper et al. study and this current study identified nests that survived baiting near bait stations. Surviving nests excavated 3 weeks after bait application displayed various levels of activity but all showed evidence of queen survival. Three surviving nests identified by Harper et al. within 100 m of baits were active 6 weeks after bait application. Queen survival was not measured in that study. Optimistically, one might hope that despite nest activity after treatment, the absence of surviving queens would effectively mean the nest is reproductively dead (with the exception of workers potentially producing males). Further research on the long-term survival of nests post-treatment is needed to identify rates of new queen mortality and whether this can be increased by adjusting the timing of bait application.

Logistic regression identified an association between higher nest density and lower nest survival after baiting. With the higher nest density we expect competition for resources to increase and, as already noted, in such situations wasps will quickly capitalise on resources close to the nest (Wilson-Rankin 2014 ), leading to increased uptake of bait and hence increased mortality. Viral and bacterial loads have been shown to increase with nesting density in Vespula pensylvanica (Loope and Wilson Rankin 2021 ), which as discussed above can predict susceptibility to pesticides (Zhu et al. 2022 ). Additionally, nest drift has been observed in social wasps including Vespula vulgaris, where individuals may temporarily inhabit and even forage for a nest that is not their own (Sumner et al. 2007 ; Santoro et al. 2019 ). This behaviour increases with nesting density and may increase a nest’s exposure to toxic baits.

In this study bait was available to wasps for only 3 days due to constraints from time and weather. This bait availability may have not been sufficient and may explain the lower level of mortality compared to Edwards et al. ( 2017 ). Their study used a similar 50 m by 300 m baiting strategy and was able to produce mean declines in traffic rate of 93% or more by 20 days post-baiting. Mean decline in traffic rate in our study only reached 88% by this same time. Baits were available to wasps in the Edwards et al. study for between 7 and 13 days, significantly longer than was possible in this current study, which may have allowed greater uptake of bait and more significant declines in wasp activity. Harris and Etheridge ( 2001 ) observed approximately 40% of bait was removed after 5 days. By comparison, we estimate less than 20% of bait placed out in our study was consumed after 3 days. Harris and Etheridge used a higher bait density than used in this study. Additionally, both Harris and Ethridge ( 2001 ) and Edwards et al. ( 2017 ) commenced baiting earlier in the year than was possible in our study, which could have influenced bait uptake. An earlier baiting date and longer duration of bait access may have reduced the number of new season queens that were observed to survive in our study. Nevertheless, despite weather constraints leading to reduced baiting duration, this study highlighted variation in baiting efficacy over a landscape. It will be important to address this source of variation when designing future baiting programs.

Though not identified in the present analysis, temperature is known to be a predictor of wasp foraging activity (Kasper et al. 2008 ). Camera footage of the two wasp nests shows evidence of a bimodal pattern of activity throughout the day. The results of our own analysis are based on only two nests and so we must be careful not to overstate our findings. It is possible, however, to see patterns of high daily wasp activity in the morning, followed by another spike in the afternoon. This pattern is not perfect and likely heavily influenced by local climate but has been observed in other wasp species such as by Santos et al. ( 2010 ). These authors observed social wasp foraging rates spike twice throughout the day, once in the morning and once in the later afternoon. This bimodal foraging pattern has also been described in social insect pollinators including bees ( Apis cerana and Bombus spp.) as well as Lepidopterans and Dipterans, likely to avoid overheating in the midday sun (Xu et al. 2021 ). The pattern appears weakest in the data collected in our study, on days that were overcast and raining (21 and 22 March) (Fig.  7 ). Wasps were still observed to be foraging during these wetter days though. During one particularly heavy downpour, two nests were observed to have wasps entering and exiting with surprisingly high frequency. Kasper et al. ( 2008 ) showed that in Vespula germanica foraging activity drops by around 30% during rain but recovers rapidly once precipitation stops. Their data also showed foraging began and ended slightly before and after daylight, respectively. This pattern is similar to what we observed in the two V. vulgaris nests observed in this study.

The spatial arrangement of the bait stations was predetermined based on a regular 50 m × 300 m grid superimposed on the landscape. The placement of these baits was not influenced by the surrounding environment except in the cases of researchers being unable to reach the specific point (due to a steep gully or ravine). While convenient for the design of baiting programs, it is inconceivable that wasps choose their nesting location in the same way. The nests discovered in this study were shown to be spatially aggregated. Nest site selection in other hymenopteran groups has been shown to be driven by factors such as correct substrate type, optimal thermal conditions, resource distribution and patterns of dispersal (Strassmann 1991 ; Thomas 2002 ; Antoine and Forrest 2021 ; Veldtman et al. 2021 ). A lack of optimum nesting conditions can enhance this aggregated pattern of nesting (Potts and Wilmer 1997 ). It is likely that some aspect of the environment is dictating the distribution of wasp nests in our treatment site. Given we found that distance to bait is a predictor of nest mortality, placing baits close to nests would be ideal in order to increase baiting efficacy. Baiting would be optimised by targeting the pesticide to where wasp nests are located though currently, we lack the sufficient knowledge to be able to successfully predict nest locations. More research should be conducted on what informs wasp nesting behaviours, to focus baiting efforts where nests are most aggregated.

To achieve eradication of invasive social insects like wasps, control methods must be highly effective (Phillips et al. 2019 ; Lester et al. 2020 ; Howse et al. 2023 ). It is important to understand and learn from both successful and failed control programmes, especially identifying why some individual colonies survive while others die. Ongoing research is needed to achieve goals of large-scale eradication programs such as the ambitious ‘Predator Free New Zealand’ initiative highlighting this sentiment (Russell et al. 2015 ; Kopf et al. 2017 ). While our study area encompassed a combined area of approximately 80 hectares, it was limited to a single treatment site. Despite the difficulties of research at large scales we highlight its importance if we are to achieve landscape-scale results.

Analysis identified that bait efficacy is heavily influenced by both the size and distribution of wasp nests in the environment. Bait uptake was shown to be variable and the spatial distribution of the wasp nests themselves exhibit significant clustering. Investigating the drivers of nest site selection and bait uptake in different environments may allow for a more advanced, flexible baiting arrangement that could maximise bait discovery and efficacy. In the meantime, maximising baiting duration may increase the likelihood of all nests encountering bait, which would target those smaller nests that appear to more effectively avoid baiting, as well as larger nests which appear to better tolerate bait exposure.

Author contributions

M W F Howse and P J Lester contributed to the study conception and design. Data collection was performed by M W F Howse and A Reason. Analyses were performed by M W F Howse and J Haywood. The first draft of the manuscript was prepared by M W F Howse, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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We would like to thank Stephen Harris for allowing us to conduct this research on the Hossack Station and for his generous contributions to the funding of this research. Thank you to all of those in the Lester Lab group who provided feedback during the writing of this manuscript.

Open Access funding enabled and organized by CAUL and its Member Institutions. This work was supported by Stephen Harris and Te Herenga Waka Victoria University of Wellington.

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Howse, M.W.F., Reason, A., Haywood, J. et al. Improving wasp control by identifying likely causes of eradication failure. J Pest Sci (2024). https://doi.org/10.1007/s10340-024-01788-9

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