Pheromone Trails Research Articles

Enhanced Ant Colony Based VM Selection and Consolidation for Energy Conservation

Cloud Computing (CC) involves extensive data centers with numerous computing nodes that consume significant electrical energy. Researchers have identified high service-level agreement (SLA) violations and excessive energy consumption (EC) as major challenges in CC. Traditional approaches often focus on reducing EC but tend to overlook SLA violations, particularly when selecting Virtual Machines (VMs) from overloaded hosts. To address these issues, this paper introduces the Enhanced Ant Colony Optimization (EACO) algorithm, aims to reduce high EC and SLA violations by utilizing a unique approach where the best-performing ant explores movement patterns and identifies distances between movements. The algorithm comprises three key steps: tracking pheromone trails, updating pheromones and selecting the cities (VMs). The effectiveness of EACO was validated through simulations using CloudSim. Compared to existing techniques, EACO demonstrated a significant reduction in EC, achieving approximately 41-44% lower energy consumption than the traditional Ant Colony Optimization (ACO) algorithm when applied to Planet Lab data. This suggests that EACO offers a more efficient and stable solution for managing EC and SLA violations in cloud environments.

Ecological principles for the evolution of communication in collective systems.

Communication allows members of a collective to share information about their environment. Advanced collective systems, such as multicellular organisms and social insect colonies, vary in whether they use communication at all and, if they do, in what types of signals they use, but the origins of these differences are poorly understood. Here, we develop a theoretical framework to investigate the evolution and diversity of communication strategies under collective-level selection. We find that whether communication can evolve depends on a collective's external environment: communication only evolves in sufficiently stable environments, where the costs of sensing are high enough to disfavour independent sensing but not so high that the optimal strategy is to ignore the environment altogether. Moreover, we find that the evolution of diverse signalling strategies-including those relying on prolonged signalling (e.g. honeybee waggle dance), persistence of signals in the environment (e.g. ant trail pheromones) and brief but frequent communicative interactions (e.g. ant antennal contacts)-can be explained theoretically in terms of the interplay between the demands of the environment and internal constraints on the signal. Altogether, we provide a general framework for comparing communication strategies found in nature and uncover simple ecological principles that may contribute to their diversity.

Influence of biotic and abiotic factors on the performance of service dogs of different genotypes

Aim. In the current situation of Russia's military invasion of Ukraine, national security, territorial integrity and state sovereignty are extremely important for the country's dog services. The benefits of using dogs are much greater than the costs of their maintenance and training. Dogs search for explosive devices, missing soldiers and their remains, detain criminals, and guard military facilities. Different breeds of service dogs are preferred for each special task. Purpose: to study the methods of training service dogs of different breeds of detection service by scent trail and to analyse certain factors affecting the quality of the dog's work. Methods. The research on the topic of the scientific work was conducted in the conditions of the dog training center of the Ministry of Internal Affairs of Ukraine in Vinnytsia region. The following methods were used in the research process: zootechnical, analytical, calculation, biometric. The research materials were used for the study of German Shepherd, Labrador Retriever and Rottweiler dogs, which underwent a general training course and special training for detective work on human scent trails. Results. Service dogs have been used in human life for centuries to protect important facilities and territories, mine clearance, customs service, detention of criminals, search and rescue of people. Training of different breeds of dogs for a particular type of activity, painstaking and careful work of dog handlers, on which the results of dog performance in different conditions of their use depend. Conclusions. The testing of sniffer dogs for human scent detection was carried out in accordance with the existing rules and methods used in the dog training centers of the National Police of Ukraine. The conditions of keeping and feeding the dogs met the standards and recommendations, taking into account the breed, age, live weight, specifics of work and workload, which allowed us to obtain reliable results in the experiments. In the organisation of training and coaching of service dogs, all our studied factors will help dog handlers to use the dog methodically and competently in real circumstances.

Differential Effects of Ephemeral and Stable Predator Chemical Cues on Spider Antipredator Behaviour

Semiochemicals left by predators in their foraging area can be utilised by prey to avoid predation. The range of predators’ chemical cues with contrasting degradation rates might provide information of different quality, potentially allowing prey to differentiate between the immediate and the longer-term presence of predators in a location. So far, knowledge about the roles of volatile versus stable chemical cues in informing predation risk is limited. We here seek to disentangle the role of ephemeral trail pheromones compared to persistent cuticular hydrocarbons of ants (predators) on the antipredator behaviour of juvenile spiders (prey), with the expectation that volatile semiochemicals induce avoidance behaviour in spiders at a higher rate compared to stable cues. We allowed the spiders to choose between sites with and without ant cues separately for volatile trail pheromones and stable hydrocarbons. Unexpectedly, spiders avoided the presence of persistent cuticular hydrocarbons more clearly than the highly volatile trail pheromone. This underscores the widespread impact of these stable cues on the avoidance behaviour of potential intraguild prey. The response to trail pheromones was unclear, possibly because spiders always encounter these cues simultaneously with visual and vibratory cues from ants; hence, trail pheromones may not contain any additional information, hindering the evolution of the ability to detect them.

Guatemalan beaded lizards (Helodermatidae: Heloderma charlesbogerti) navigate and follow a scent trail in maze tasks.

Maze studies have provided substantial information about nonhuman cognition, such as insights on navigational strategies, spatial memory, and choice discriminations. This knowledge can aid in how we understand the foraging strategies of many animals, particularly understudied and endangered species, such as the Guatemalan beaded lizard (Heloderma charlesbogerti). These actively foraging lizards rely on chemoreception to locate prey, but it is unknown to what extent they engage in olfaction and vomerolfaction to hunt and navigate their environment. We investigated how Guatemalan beaded lizards moved through a physical maze. When navigating an eight-arm radial maze with all arms baited, lizards tended to turn into the immediately adjacent arm in a single direction, similar to other reptiles that have been tested in radial arm mazes. In a T-maze, the lizards had to discriminate between arms that contained scent and no-scent from a distance. They were generally unable to choose the baited (correct) arm at levels greater than chance, indicating an inability for this discrimination. With the addition of a scent trail, however, all lizards chose the baited arm at levels significantly above chance, and this increased accuracy was correlated with increased latency to make the arm choice. The lizards also demonstrated a decreased rate of tongue flicking as proximity to reward increased. Guatemalan beaded lizards can efficiently navigate a radial arm maze and can successfully use vomerolfaction with substrate-borne cues to locate prey, but they appear to have minimal olfaction abilities when sensing from a distance. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Ants (Lasius niger) deposit more pheromone close to food sources and further from the nest but do not attempt to update erroneous pheromone trails

Many social insects use pheromone trails to recruit workers to resources. Pheromone trails have some limitations, however. Two major limitations are that they evaporate over time, meaning that more distant locations may be more difficult to recruit precisely to, and that they cannot be removed if laid erroneously, or if they become outdated. Here, we ask whether Lasius niger ants can adaptively modulate pheromone deposition to overcome these limitations. Specifically, we first ask whether ants which have followed a pheromone trail to a wrong (unrewarded) location upregulate pheromone deposition to the correct food location to compete with the erroneous trail. They do not. Then, we examine how food distance and proximity to food influence pheromone deposition. We find that ants deposit up to 22 times more pheromone within 10 cm of a food source compared to when they are about to reach the nest. Moreover, they deposit up to four times more pheromone next to a food source placed 100 cm away from the nest as compared to one placed 20 cm away from the nest. Lasius niger ants thus do not display a mechanism for overcoming outdated or erroneously-laid trails by strengthening correct alternatives in our experiment, but do strongly upregulate recruitment to more distant food sources, which are more difficult to locate and recruit to.

Enforced specificity of an animal symbiosis.

Insect diversification has been catalyzed by widespread specialization on novel hosts - a process underlying exceptional radiations of phytophagous beetles, lepidopterans, parasitoid wasps, and inordinate lineages of symbionts, predators and other trophic specialists. The strict fidelity of many such interspecies associations is posited to hinge on sensory tuning to host-derived cues, a model supported by studies of neural function in host-specific model species. Here, we investigated the sensory basis of symbiotic interactions between a myrmecophile rove beetle and its single, natural host ant species. We show that host cues trigger analogous behaviors in both ant and symbiont. Cuticular hydrocarbons - the ant's nestmate recognition pheromones - elicit partner recognition by the beetle and execution of ant grooming behavior, integrating the beetle into the colony via chemical mimicry. The beetle also follows host trail pheromones, permitting inter-colony dispersal. Remarkably, the rove beetle also performs its symbiotic behaviors with ant species separated by ~95 million years, and shows minimal preference for its natural host over non-host ants. Experimentally validated agent-based modeling supports a scenario in which specificity is enforced by physiological constraints on symbiont dispersal, and negative fitness interactions with alternative hosts, rather than via sensory tuning. Enforced specificity may be a pervasive mechanism of host range restriction of specialists embedded within host niches. Chance realization of latent compatibilities with alternative hosts may facilitate host switching, enabling deep-time persistence of obligately symbiotic lineages.

ADVANCED TECHNIQUES IN MULTI-LABEL TEXT CLASSIFICATION: INTEGRATION OF BETA ANT COLONY AND DEEP LEARNING APPROACHES

The rapid growth of unstructured textual data in various domains has necessitated the development of sophisticated techniques for multi-label text classification. Traditional methods often struggle with handling the complexity and interdependence of multiple labels, leading to suboptimal performance. This paper presents an advanced approach that integrates the Beta Ant Colony Optimization (BACO) algorithm with deep learning techniques for multi-label text classification. The BACO algorithm effectively explores the feature space and selects relevant features by leveraging pheromone trails, while the deep learning model captures intricate patterns and relationships within the textual data. The integration of these two methodologies aims to enhance the efficiency and accuracy of multi-label classification tasks, particularly in domains where label dependency is prominent. Empirical evaluations on benchmark datasets demonstrate that the proposed hybrid approach outperforms existing state-of-the-art techniques in terms of precision, recall, F1-score, and computational efficiency. The findings suggest that combining heuristic optimization algorithms with deep learning can significantly improve multi-label text classification performance, providing a robust solution for real-world applications.

Environmental memory facilitates search with home returns

Search processes in the natural world are often punctuated by home returns that reset the position of foraging animals, birds, and insects. Many theoretical, numerical, and experimental studies have now demonstrated that this strategy can drastically facilitate search, which could explain its prevalence. To further facilitate search, foragers also work as a group: modifying their surroundings in highly sophisticated ways, e.g., by leaving chemical scent trails that imprint the memory of previous excursions. Here, we design a controlled experiment to show that the benefit coming from such environmental memory is significant even for a single, nonintelligent searcher that is limited to simple physical interactions with its surroundings. To this end, we employ a self-propelled bristle robot that moves randomly within an arena filled with obstacles that the robot can push around. To mimic home returns, we reset the bristle robot's position at constant time intervals. We show that trails created by the robot give rise to a form of environmental memory that facilitates search by increasing the effective diffusion coefficient. Numerical simulations and theoretical estimates designed to capture the essential physics of the experiment support our conclusions and indicate that these are not limited to the particular system studied herein. Published by the American Physical Society 2024

Optimizing School Bus Routes With Ant Colony Optimization: A Dynamic Approach To Sustainable Transportation Logistics

In response to the escalating demands of modern transportation and logistics within the educational sector, this paper presents a novel exploration of Ant Colony Optimization (ACO) applied to school bus routing, a complex subset of the Vehicle Routing Problem (VRP). Traditional methods of addressing VRPs have proven insufficient in navigating the intricate requirements and dynamic nature of routing challenges, necessitating the adoption of more sophisticated heuristic and metaheuristic strategies. By harnessing ACO, inspired by the path-finding capabilities of ants via pheromone trails, this study introduces an innovative approach to dynamically optimize school bus routes. This optimization not only aims for efficiency in routing but also emphasizes environmental considerations and cost reduction. This research extends beyond the theoretical framework of ACO, incorporating practical applications and simulations that reflect real-world conditions such as fluctuating student attendance and varying traffic patterns. Through a comprehensive analysis that includes the development of a Python-based ACO model, calculation of transition probabilities, and a simulation of routing strategies, we demonstrate the algorithm's robustness and versatility. Additionally, we address critical factors such as time windows and bus capacity constraints, underscoring the model's adaptability to the multifaceted dimensions of school bus routing. The findings from this study highlight the significant advantages of applying ACO to VRPs, showcasing notable improvements in route efficiency, fuel consumption, and overall logistical execution compared to conventional routing methods. This research not only contributes to the existing body of knowledge on VRPs and ACO but also sets the stage for future explorations into dynamic routing optimization. It opens avenues for integrating advanced predictive models and real-time data analysis, promising further enhancements in transportation logistics and the potential for broad application across various sectors.

DB-ACO: A Deadline-Budget Constrained Ant Colony Optimization for Workflow Scheduling in Clouds

With the development of cloud computing, a growing number of workflows are deployed in cloud platform that can dynamically provide cloud resources on demand for users. In clouds, one basic problem is how to schedule workflow under the deadline constraint and minimize the execution cost. As the capability of cloud resources getting higher, the required cost is also rising. Capability of some resources exceeds the need of users, which leads to higher cost, and the budget of users should be considered. In this paper, a novel scheduling algorithm, named DB-ACO, is proposed to minimize the execution cost for the workflow with deadline and budget constraints. DB-ACO is verified on four typical scientific workflows, and the experiments results show it outperforms four state-of-the-art methods, especially for CyberShake. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Budget and deadline are important requirements for users in cloud computing, which are used as constraints. Extensive works have been devoted to minimize the cost of workflows execution with different scheduling strategies. However, most of them only consider one single constraint and assume the constraint is simple and loose, which is impractical in actual scenarios due to higher requirement of users. This paper investigates a novel scheduling algorithm DB-ACO to optimize cost under budget and deadline. DB-ACO combines heuristic and meta-heuristic, it uses ant colony optimization to optimize the execution cost under the deadline and budget constraints: each ant sorts tasks on the basis of the combination of the pheromone trail and heuristic information, the deadline and budget are distributed fairly to each task by a novel distribution method, then the service selection rules are introduced to build solution.

Foraging behaviour of Pheidole latinoda: An impetus for employing a heuristic approach to address optimization challenges

The primary goal of the binary model in this study was to understand the convergence pattern of the Pheidole latinoda ants. Forager and scout ants on the hunt for food use path integration. When they find a food source, they leave a trail pheromone to alert other nest mates. Every ant starts following that trail and reinforces it on their way back home. To investigate the ant convergence pattern, binary and ternary bridges of varying lengths are used. Each bridge is built in such a way that one end is connected to a food source whilst the other end is connected to the nest. The food source is surrounded by water-filled islands. The Pheidole latinoda ant's convergence pattern has been observed following the successful installation of a bridge near the ants' nest. This species took between 1-3 and 3-4minutes to find the shortest possible path. Numerous studies looking for optimal solutions, such as those addressing the challenges of travelling salesmen, routing in communication networks, etc., may use this convergence or path optimization as their new starting point.

Improving Data Delivery in Unreliable Networks Using Network Coding and Ant-Colony Optimization

Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes.

Termites can learn

It is generally believed that termites can’t learn and are not “intelligent”. This study aimed to test whether termites could have any form of memory. A Y-shaped test device with one release chamber and two identical test chambers was designed and constructed by 3D printing. A colony of damp wood termites was harvested from the wild. Worker termites were randomly selected for experiment. Repellent odors that could mimic the alarm pheromone for termites were first identified. Among all substances tested, a tea tree oil and lemon juice were found to contain repellent odors for the tested termites, as they significantly reduced the time that termites spent in the chamber treated with these substances. As control, a trail pheromone was found to be attractive. Subsequently, a second cohort of termites were operant conditioned by punishment using both tea tree oil and lemon juice, and then tested for their ability to remember the path that could lead to the repellant odors. The test device was thoroughly cleaned between trials. It was found that conditioned termites displayed a reduced tendency to choose the path that led to expectant punishment as compared with naïve termites. Thus, it is concluded that damp wood termites are capable of learning and forming “fear memory”, indicative of “intelligence” in termites. This result challenges established presumption about termites’ intelligence.

Neuroregulation of foraging behavior mediated by the olfactory co-receptor Orco in termites

Olfaction is critical for survival because it allows animals to look for food and detect pheromonal cues. Neuropeptides modulate olfaction and behaviors in insects. While how the neuroregulation of olfactory recognition affects foraging behavior in termites is still unclear. Here, we analyzed the change after silencing the olfactory co-receptor gene (Orco) and the neuropeptide Y gene (NPY), and then investigated the impact of olfactory recognition on foraging behavior in Odontotermes formosanus under different predation pressures. The knockdown of Orco resulted in the reduced Orco protein expression in antennae and the decreased EAG response to trail pheromones. In addition, NPY silencing led to the damaged ability of olfactory response through downregulating Orco expression. Both dsOrco- and dsNPY-injected worker termites showed significantly reduced walking activity and foraging success. Additionally, we found that 0.1 pg/cm trail pheromone and nestmate soldiers could provide social buffering to relieve the adverse effect of predator ants on foraging behavior in worker termites with the normal ability of olfactory recognition. Our orthogonal experiments further verified that Orco/NPY genes are essential in manipulating termite olfactory recognition during foraging under different predation pressures, suggesting that the neuroregulation of olfactory recognition plays a crucial role in regulating termite foraging behavior.

No evidence that recruitment pheromone modulates olfactory, visual, or spatial learning in the ant Lasius niger

Pheromones are perhaps the most common form of intraspecific communication in the animal kingdom and used in various contexts. Their modulatory potential on cognitive processes has been demonstrated in both vertebrates and invertebrates. Particularly interesting in this regard are social insects, due to their extensive use of pheromones to organise collective behaviour. Recruitment pheromones might be expected to encourage learning, but could also hinder learning due to a blocking effect, whereby the pheromone already partially predicts the reward, hindering further cues being associated with the reward. Here, we use free-running learning assays using realistic pheromone strength to test for a modulation effect on learning in the black garden ant Lasius niger. We found no evidence that learning in three modalities (olfactory, visual, and spatial) is affected by the presence of a realistic pheromone trail. Interestingly, this is in contrast to findings in honeybees. The fact that associative learning does not seem to be influenced by recruitment pheromone in L. niger and reportedly the Argentine ant, while it is in honeybees, the possibly best-studied social insect species, is noteworthy. We speculate that a species-specific importance of social information use could drive modulatory effects of pheromones on a wide range of cognitive processes.Significance statementPheromones have been shown to modulate associative learning in a variety of animals. Among social insects, attractive pheromone has been found to enhance associative olfactory learning in honeybees but not in ants. In ants, recruitment pheromone predicts a food source; therefore, it might hinder learning of a new cue for a food reward. We use a free-running learning assay to test for an effect of trail pheromone on associative learning in three different modalities—olfactory, spatial, visual—in Lasius niger, but find no evidence of any effect. Our learning assay demonstrated fast olfactory learning, moderate spatial learning, and no visual learning after only one training visit. Based on our findings, and findings in two other ant species, we speculate that the ecological foraging conditions of mass-recruiting ants, i.e. following a trail, have not favoured a modulation potential of recruitment pheromone opposed to attractive pheromone in honeybees.

Route retracing: way pointing and multiple vector memories in trail-following ants.

Maintaining positional estimates of goal locations is a fundamental task for navigating animals. Diverse animal groups, including both vertebrates and invertebrates, can accomplish this through path integration. During path integration, navigators integrate movement changes, tracking both distance and direction, to generate a spatial estimate of their start location, or global vector, allowing efficient direct return travel without retracing the outbound route. In ants, path integration is accomplished through the coupling of pedometer and celestial compass estimates. Within path integration, it has been theorized navigators may use multiple vector memories for way pointing. However, in many instances, these navigators may instead be homing via view alignment. Here, we present evidence that trail-following ants can attend to segments of their global vector to retrace their non-straight pheromone trails, without the confound of familiar views. Veromessor pergandei foragers navigate to directionally distinct intermediate sites via path integration by orienting along separate legs of their inbound route at unfamiliar locations, indicating these changes are not triggered by familiar external cues, but by vector state. These findings contrast with path integration as a singular memory estimate in ants and underscore the system's ability to way point to intermediate goals along the inbound route via multiple vector memories, akin to trapline foraging in bees visiting multiple flower patches. We discuss how reliance on non-straight pheromone-marked trails may support attending to separate vectors to remain on the pheromone rather than attempting straight-line shortcuts back to the nest.

ACO-DTSP Algorithm: Optimizing UAV Swarm Routes with Workload Constraints

In this research, we present a variant of the Ant Colony Optimization (ACO) algorithm tailored for optimizing routing within a limited-capacity environment for a swarm of drones. Our primary objective is to minimize task completion time while ensuring that individual node capacity constraints are never breached. Our algorithm leverages both pheromone trails and heuristic information to guide drone movements intelligently, and it dynamically updates the pheromone trail based on solution quality. This research underscores the importance of ideal routing algorithms in drone swarm systems and showcases ACO's effectiveness in optimizing such challenges. We conducted a comprehensive performance comparison against the existing methods like Christofides algorithm, Simulated Annealing, K-Opt and Lin-Kernighan Heuristic and the Brute-force method. Results indicate that our algorithm significantly improves time efficiency and is feasible for real-time environments. Furthermore, we highlight the potential for substantial enhancements in the system by deploying suitable hardware, such as quadcopters with specific capabilities.

Pavement ant extract is a chemotaxis repellent for C. elegans.

Ant behavior relies on a collection of natural products, from following trail pheromones during foraging to warding off potential predators. How nervous systems sense these compounds to initiate a behavioral response remains unclear. Here, we used Caenorhabditis elegans chemotaxis assays to investigate how ant compounds are detected by heterospecific nervous systems. We found that C. elegans avoid extracts of the pavement ant ( Tetramorium immigrans ) and either osm-9 or tax-4 ion channels are required for this response. These experiments were conducted in an undergraduate laboratory course, demonstrating that new insights into interspecies interactions can be generated through genuine research experiences in a classroom setting.

Relative efficiency of hunting methods during an incomplete Sitka black‐tailed deer eradication on Haida Gwaii, Canada

Abstract Invasive species are reducing global biodiversity. The Llgaay gwii sdiihlda—Restoring Balance project was initiated to halt and reverse biodiversity loss on six islands on Haida Gwaii, Canada through the eradication of invasive Sitka Black‐tailed deer (Odocoileus hemionus sitkensis). Invasive deer were killed using various hunting methods including bait station, boat, aerial and indicator dog hunting as well as a combination of bailing dogs with boat and aerial hunting. Cost was quantified in terms of helicopter hunting hours ($1965 CAD/hr at the time of the study) to facilitate comparisons. Efficiency was quantified as the expected number of deer per helicopter hour. Hierarchical Bayesian integrated removal models were used to estimate the change in efficiency of the various hunting methods with deer density based on a power function. The models used the number of scent trail detections by dogs at the end of the study to help parameterise the population density. At densities between 2 and 30 deer/km2, boat hunting was estimated as the most efficient method while at lower densities indicator dog hunting was most efficient. The eradication was incomplete. The estimated cost to completion using indicator dog hunting for each island was 43 helicopter hours (~$84,000) on Ramsay Island (95% CI = 19–128), 3.9 helicopter hours on Murchison Island (95% CI = 0.56–18) and no additional hours on House (95% CI = 0–1.6) though the selected method would likely have been indicator dogs with combined hunting due to the variable terrain. Lessons learned: DNA connectivity analysis can be used to determine project durability, pilot studies to confirm method efficiency in local context will increase the chances of success, local knowledge improves efficiency of hunting methods, population and efficiency estimates should be updated throughout operations to allow adaptive management, eradications on ungulates can include pauses to facilitate adaptive management, the level of certainty for determining eradication completion should be determined in advance of operations with explicit trade‐offs identified for managers.