Hunting Behavior Research Articles

The predation behavior and the prey size preferences of Antilochus coquebertii (Pyrrhocoridae) against Dysdercus koenigii (Pyrrhocoridae)

The pest status of the red cotton bug, Dysdercus koenigii (Fab.) has been changing due to the climatic changes, and appearances of new natural enemies in cotton agro-ecosystems. As a specialized predator of Dysdercus bugs, Antilochus coquebertii (Fab.) has been known, and is abundant now in cotton fields in Tamil Nadu, India. Therefore we investigated its predatory behavior and the preference against prey developmental stage in order to evaluate its performance as a natural enemy against D. koenigii. The predatory behavior of A. coquevertii adults consisted of seven feeding acts; i.e., arousal, approaching, capturing, rostral probing, paralyzing, feeding and post-feeding. Adult males were quicker than females in predatory acts including aroused, approached, captured, probed the prey by rostrum, paralyzed, and post-predatory behaviors. The predators exhibited an active hunting behavior, in which they seem to have used both olfactory and visual stimuli for orientation to the prey. The results of the prey-choice test exhibited that the second, third, fourth and fifth nymphal-stage and adult predator has a reproductive-stage preference to the second, third, fourth and fifth nymphal-stage and adult prey, respectively. The results implies that the synchronization between the occurrence of the prey and the predator could have significance for the control of the pest using the predator.

Multi-Objective Optimal Placement and Sizing of Distribution Static Compensator in Radial Distribution Networks With Variable Residential, Commercial and Industrial Demands Considering Reliability

This article presents an optimal placement and sizing of distribution static compensator (DSTATCOM) in radial distribution networks as multi-objective optimization with the objective of power loss reduction, voltage profile improvement and network reliability considering different variable demands using whale optimization algorithm (WOA). The WOA is inspired by the hunting behavior of humpback whales. The optimal location and size of DSTATCOM are considered as decision variables that are determined using the WOA. Network reliability is defined as energy not supplied (ENS) of the customers caused by line outages. The proposed method is implemented on IEEE 33-bus and Ahvaz 59-bus distribution networks. The optimal placement and sizing of DSTATCOM are investigated considering different residential, commercial and industrial loads, and also the effect of incorporating reliability is evaluated on power loss, minimum voltage and ENS. The simulation results show that the power loss and ENS are declined and also voltage profile is improved by determining the location and size of DSTATCOM in the networks in different loading conditions using the WOA. The obtained results demonstrate that considering reliability further reduces the ENS and more customer demand is met. The power loss decreases 21.60%, 30.71%, and 22.03% for residential, commercial and industrial loads in the 33-bus network and 26.86%, 31.94%, and 24.39% for the 69-bus network, respectively compared to base networks. Moreover, the performance of the WOA is compared with previous studies and the results indicate the superiority of the proposed method.

Opportunistic predation events of bats entangled in mist nets by margay Leopardus wiedii (Schinz, 1821) in northwest Honduras: recommendations to avoid preventable casualties.

Predation of entangled bats captured in mist-nets is common but infrequently documented. As such, this welfare issue is often not considered when mist-netting surveys are being designed. Margays (Leoparduswiedii) are small neotropical cats that are known to have a varied diet and exhibit opportunistic hunting behaviour. Despite bats not having been frequently reported as a prey item for margays, studies on this felid’s feeding ecology remain scarce. We discuss the potential for margays to feed on bats when they become entangled in mist nets, providing two examples from Cusuco National Park, Honduras. In light of this, we provide recommendations as to how such opportunistic predation events can be mitigated in future surveys.

QALO-MOR: Improved antlion optimizer based on quantum information theory for model order reduction

The field of optimization science is proliferating that has made complex real-world problems easy to solve. Metaheuristics based algorithms inspired by nature or physical phenomena based methods have made its way in providing near-ideal (optimal) solutions to several complex real-world problems. Ant lion Optimization (ALO) has inspired by the hunting behavior of antlions for searching for food. Even with a unique idea, it has some limitations like a slower rate of convergence and sometimes confines itself into local solutions (optima). Therefore, to enhance its performance of classical ALO, quantum information theory is hybridized with classical ALO and named as QALO or quantum theory based ALO. It can escape from the limitations of basic ALO and also produces stability between processes of explorations followed by exploitation. CEC2017 benchmark set is adopted to estimate the performance of QALO compared with state-of-the-art algorithms. Experimental and statistical results demonstrate that the proposed method is superior to the original ALO. The proposed QALO extends further to solve the model order reduction (MOR) problem. The QALO based MOR method performs preferably better than other compared techniques. The results from the simulation study illustrate that the proposed method effectively utilized for global optimization and model order reduction.

Multi-threaded Spotted Hyena Optimizer with thread-crossing techniques

Abstract This paper presents a Multi-threaded version of the Spotted Hyena Optimizer algorithm with thread-crossing techniques (MT-SHO) to improve the ability of the algorithm to explore the search space. The original algorithm is inspired by the hunting behavior of the spotted hyena. Along the different sections of the work, we explain in detail how the original algorithm simulates the spotted hyena’s behavior to optimize highly complex mathematical functions and how we handle the procedures and results of the multi-threaded version, with thread-crossing techniques that improve the ability to explore and exploit the search space by letting threads learn between them. We present the experiments used to determine the best value of the parameters used in the parallel version of the algorithm and to prove that our proposal obtains significantly good results we compare the results obtained by evaluating 24 benchmark functions with the results published for the original algorithm as well as other metaheuristic algorithms.

FIRST REPORT ON HUNTING BEHAVIOR OF MIGRATORY ORIENTAL HONEY-BUZZARD (PERNIS PTILORHYNCHUS ORIENTALIS) TOWARDS MIGRATORY GIANT HONEYBEE (APIS DORSATA DORSATA) (HYMENOPTERA: APIDAE) ON JAVA ISLAND, INDONESIA

Both Migratory Oriental Honey-buzzard (Pernis ptilorhynchus orientalis) and migratory giant honeybee (Apis dorsata dorsata) can be found in South-east Asia. The Oriental Honey-buzzard is the main predator of the giant honeybee, prey upon its honeycomb, larvae, and honey. Its existence always follows the migration of the giant honeybee. They stay on Java island during the migratory season. The giant honeybee lives in a large colony and has a powerful sting that is useful for defence against its predators. The bee is among the most dangerous animals since its threatening defensive behavior causes severe impact on the eagle and is even frequently fatal for human beings. Data collections on hunting behavior of the Oriental Honey-buzzard were based on irregular observations and interviews between the year 2003 to 2019. We categorized five hunting behaviors during data collections: flying orientation around the bee’s nest, attack on living nest, failure to collect the living nest, preying upon the newly empty nest, and transferring attack of the angry bee to people nearby. The safest hunting for the Oriental Honey-buzzard is to prey upon newly empty nest left by the honeybee. When the nest was still occupied by the bee colonies, the eagle should develop a strategy to avoid and reduce the risk of being attacked. It sometimes transfers the attack to people nearby.

Crepuscular hunting of swiftlets (Family: Apodidae) by Besra (Family: Accipitridae) in the urban areas of the Andaman Islands, India

We report the crepuscular hunting behavior by the Besra Accipiter virgatus, on the Glossy Swiftlets Collocalia esculenta affinis and the Edible-nest Swiftlets Aerodramus fuciphagus inexpectatus in urban areas the Andaman & Nicobar Islands. Unlike other raptors in the islands, the Besra hunts at twilight often in the absence of moonlight or/and artificial light. Glossy and Edible-nest Swiftlets have been ranched in human habitations and their nests harvested for livelihood support of local communities under an ex situ conservation program. Using the focal animal sampling method, we recorded the hunting behavior of the Besra (the predator) on the swiftlets (the prey) for 40h (120 min/day for 20 days) at the ex situ swiftlet colony established in a house in the Middle Andamans. The Besra made 84 hunting attempts, with the highest success rate (15.4%) between 17.00–18.00 h. The catch rate was a mean of 4±11 (SD) per day. The maximum time that was used for attempt to kill the prey was two hours. Depredation of the Edible-nest Swiftlet by the Besra could affect ex situ conservation efforts, which can also lead to economic losses and retaliation against the raptor. Restricting perch sites for the raptor around ranching houses might reduce predation risks for the swiftlets.

Drivers and facilitators of hunting behaviour in domestic cats and options for management

Abstract Domestic cats Felis catus are distinct from other domesticated animals because their phenotype and genotype are relatively unchanged. While they live with people as pets or pest controllers, they retain capacity for survival independent of human support and readily persist as feral animals. Most cats retain some propensity to express hunting behaviours, even if hunting is not required for nutrition. In some settings, depredation by cats is a threat to biodiversity conservation, leading to attempts to mitigate their impacts. We characterise drivers and facilitators of the hunting behaviour of domestic cats: evolutionary origins, diet, life history, personality and environment. Hunting is driven particularly by evolutionary constraints and associated physiological and nutritional requirements. Proximate causes of variation in hunting behaviours relate to prey availability, husbandry and degree of domestication, while early life history and personality play further roles. We review cat management approaches in terms of effectiveness, feasibility and welfare. Amongst lethal, large‐scale methods of population control, poisoning is most frequently used in cat eradications from islands. Because poisoning is challenged on welfare grounds, euthanasia is used at smaller scales and in inhabited, mainland settings. Non‐lethal approaches, primarily surgical sterilisation, are favoured by cat advocates but entail challenging logistics and scale. In attempts to inhibit predation of wild species by pet cats, owners restrict outdoor access and use collar‐mounted devices, including bells, sonic devices, collar covers and bibs. Other individual‐level interventions, such as dietary and behavioural enrichment, some of which may improve cat welfare, have potential, but effects on hunting remain untested. Understanding and managing the hunting behaviour of cats are complex challenges. We highlight drivers and facilitators of this behaviour, representing starting points for formulating solutions that might be acceptable to cat owners and wider groups of people who value cat welfare, while also being effective for wildlife conservation.

A beamforming study of the linear antenna array using grey wolf optimization algorithm

<p><br />The grey wolf optimization (GWO) algorithm is considered an inspired meta-heuristic algorithm, which inspired by the social hierarchy and hunting behavior of the grey wolves. GWO has a high-performance capability of solving constrained, as well as unconstrained optimization problems. In this paper, the beamforming of smart antennas in a code division multiple access system based on the GWO algorithm is investigated. The sidelobe level (SLL) is minimized along with peak sidelobe level reduction, as well as an optimal beam pattern has been accomplished by using GWO to uniform linear antenna arrays. In this work, an amplitude is introduced as constant, while the interspacing distance between antenna array elements and the number of elements in a linear array are variables. The simulation results show that a faster convergence and likely high accurate beamforming are gained using GWO based method. Finally, it is shown that the GWO outperforms the genetic algorithm (GA) based method.</p>

Experimental Investigations of Hunting Behavior in the Mountain Voles Alticola strelzowi and Alticola Tuvinicus (Rodentia, Cricetidae)

In laboratory experiments, purposeful inter-relations with moving insects in the “predator–prey” manner have been revealed and described in two species of herbivorous mountain voles: the Tuva vole and the flat-headed vole. The patterns of the hunting behavior are similar in these species. The appearance of the full hunting stereotype in mountain voles does not require preliminary experience and does not improve, which indicates its innate nature. Unlike the rodent species studied earlier, mountain voles demonstrate “storing” behavioral patterns when operating with live prey. According to the characteristics of the interactions with prey, mountain voles are among the most successful and effective hunters for moving insects. Regarding the hunting tactics and the manners of their manipulations with prey, mountain voles are similar to the most “predatory” of omnivorous hamsters, Eversmann’s hamsters, but differ from them in an optional manifestation of the hunting stereotype. The frequency of occurrence of the stereotype and the success of hunting in mountain voles are twice as high as in the previously studied herbivorous narrow-head vole. Characteristics of a hunting behavior so unexpected in herbivorous rodents were revealed in mountain voles for the first time. The expansion of the diet due to the hunt for insects can be attributed to dwelling in arid places with a deficit of plant food resources.

Molecular identification of bird species in the diet of the bird‐like noctule bat in Japan

AbstractWe investigated the birds in the diet of the bird‐like noctule (Nyctalus aviator) through DNA amplification from feather remains found in faecal pellets. Our goal was to confirm whether N. aviator preys on nocturnally migratory species, as does its European relative N. lasiopterus, and to gain insight into this hunting strategy (e.g. on the wing vs. from cavities). The diversity and the characteristics of the birds found in the faecal remains indicate that in Japan N. aviator employs a similar hunting strategy on songbirds as N. lasiopterus in Iberia. Both noctules are primarily generalist insectivores that prey seasonally on songbirds of 5‐25 g body mass during their migration and probably hunt them at high altitude. Accordingly, predation on birds seems to be a strategy that appears in distant points across the Palaearctic and is not the result of local specialization. We hypothesize that the bird hunting behaviour appeared during evolution as a trophic jump from a pre‐existing hunting behaviour on the many insects that carry out seasonal nocturnal migrations in temperate zones at high altitude.

A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home

Abstract In this paper, the min-conflict local search algorithm (MCA) is hybridized with the grey wolf optimizer (GWO) for the power scheduling problem in smart home (PSPSH), and the proposed method is called GWO-MCA. MCA is utilized as a new operator of GWO to improve its exploitation capability in addressing constraint satisfaction problems, particularly scheduling problems. GWO is one of the most efficient metaheuristic algorithms which mimics the hunting behavior of grey wolves. PSPSH is a problem of scheduling smart home appliances in accordance with a dynamic pricing scheme(s) to flatten users’ power consumption. PSPSH’s objectives are to reduce electricity bills, improve user comfort, and maintain power systems’ performance. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. GWO-MCA is tested by using up to 36 appliance operations for 7 days. To show the effect of MCA on the convergence behavior of optimization problems, MCA is utilized as a new operator on other five popular optimization methods: genetic algorithm, particle swarm optimization, wind-driven optimization, antlion optimizer, and enhanced differential evolution. Interestingly, MCA shows a high impact on the performance of such algorithms. In addition, GWO-MCA achieves a better schedule than all compared MCA-Based methods in tackling PSPSH. In addition, the results of GWO-MCA are compared with those of three state-of-the-art hybrid methods to verify the GWO-MCA performance. The GWO-MCA excels the other comparative methods in almost all datasets used. Besides, the GWO-MCA method is compared with 20 state-of-the-art methods using their datasets. Again, GWO-MCA is able to outperform them for almost all datasets used.

Load balancing in the internet of things using fuzzy logic and shark smell optimization algorithm

PurposeBased on the positive features of the shark smell optimization (SSO) algorithm, the purpose of this paper is to propose a method based on this algorithm, dynamic voltage and frequency scaling (DVFS) model and fuzzy logic to minimize the energy consumption of integrated circuits of internet of things (IoT) nodes and maximize the load-balancing among them.Design/methodology/approachLoad balancing is a key problem in any distributed environment such as cloud and IoT. It is useful when a few nodes are overloaded, a few are under-loaded and the remainders are idle without interrupting the functioning. As this problem is known as an NP-hard one and SSO is a powerful meta-hybrid method that inspires shark hunting behavior and their skill to detect and feel the smell of the bait even from far away, in this research, this study have provided a new method to solve this problem using the SSO algorithm. Also, the study have synthesized the fuzzy logic to counterbalance the load distribution. Furthermore, DVFS, as a powerful energy management method, is used to reduce the energy consumption of integrated circuits of IoT nodes such as processor and circuit bus by reducing the frequency.FindingsThe outcomes of the simulation have indicated that the proposed method has outperformed the hybrid ant colony optimization – particle swarm optimization and PSO regarding energy consumption. Similarly, it has enhanced the load balance better than the moth flame optimization approach and task execution node assignment algorithm.Research limitations/implicationsThere are many aspects and features of IoT load-balancing that are beyond the scope of this paper. Also, given that the environment was considered static, future research can be in a dynamic environment.Practical implicationsThe introduced method is useful for improving the performance of IoT-based applications. We can use these systems to jointly and collaboratively check, handle and control the networks in real-time. Also, the platform can be applied to monitor and control various IoT applications in manufacturing environments such as transportation systems, automated work cells, storage systems and logistics.Originality/valueThis study have proposed a novel load balancing technique for decreasing energy consumption using the SSO algorithm and fuzzy logic.

A New Bio-Inspired Algorithm Based on the Hunting Behavior of Cheetah

Soft computing is recognized as the fusion of methodologies mainly designed to model and formulate solutions to real-world problems that are too difficult to model mathematically. The grey wolf optimizer (GWO) algorithm is the recently proposed bio-inspired optimization algorithm that is mainly based on their foraging and hunting behavior. This GWO is proved as the recent and best in solving complex problems, but they too face some drawbacks of low solving precision, slow convergence, and bad local searching ability. In order to overcome the shortcomings of the existing algorithms, this paper is intended to propose a novel algorithm based on the foraging behavior of the cheetah. The cheetah is well known for their leadership hierarchy, decision making, and efficient communication capabilities between their teammates during group hunting. The famous benchmark functions such as unimodal and multimodal functions are being chosen as the testbed, and the experiments are performed on them. The proposed scheme outperforms in terms of computational time and optimal solution.

BMDA: applying biogeography-based optimization algorithm and Mexican hat wavelet to improve dragonfly algorithm

One of the methods for solving optimization problems is applying metaheuristic algorithms that find near to optimal solutions. Dragonfly algorithm is one of the metaheuristic algorithms which search problem space by the inspiration of hunting and emigration behavior of dragonflies in nature. However, it suffers from the premature convergence of the population to an undesirable point in the detection ability (global search). In this research, an improved dragonfly algorithm called BMDA (applying Biogeography-based algorithm, Mexican hat wavelet, and Dragonfly algorithm) is presented to resolve the premature convergence in high workloads by creating a mutation phase based on the combination of the biogeography-based optimization (BBO) migration process and the Mexican hat wavelet transform in dragonfly algorithm (DA). The algorithm was evaluated for the mean error in comparison with standard dragonfly algorithm (DA), Memory-based Hybrid Dragonfly Algorithm (MHDA), chaotic dragonfly algorithm version 9 (CDA9), Adaptive_DA algorithm, bat algorithm (BAT), particle swarm optimization algorithm (PSO), raven roosting optimization (RRO) and whale optimization algorithm (WOA) using the CEC2017 benchmark functions. The implementation results of the proposed BMDA algorithm applying different benchmark functions outweighed the DA-based algorithm, MHDA algorithm, CDA9 algorithm, Adaptive_DA algorithm, BAT algorithm, PSO algorithm, RRO, and WOA algorithms in terms of mean error.

Notes on the diet and foraging habits of the Giant Barred Frog Mixophyes iteratus

ABSTRACT Australian anurans generally display little prey specialisation. Most data available of frog diet is limited to stomach content and faecal analysis with few direct observations of prey capture. Limited information is available on the diet of adult Giant Barred Frogs Mixophyes iteratus. In this paper I report on several observations of M. iteratus feeding on a variety of prey from various taxa. These observations suggest that M. iteratus are opportunistic, ambush predators with a broad diet. Their morphology and hunting behaviour align with existing models of amphibian foraging patterns.

Maximum Power Point Tracking using Grey Wolf Technique Under Fast-Changing Irradiance

In this paper, maximum power point tracking (MPPT) using Grey wolf optimization (GWO) algorithm is presented using MATLAB/Simulink. As we know that meta-heuristic or nature-inspired algorithm has proven to be superior in performance compared to the conventional MPPT methods. Grey Wolf optimization algorithm is a meta-heuristic algorithm based on the hunting behaviour of grey wolves. The proposed system includes modelling of PV system under changing irradiance and the MPPT control is driven by GWO algorithm. Most of the conventional MPPTs are unable to track multiple peaks and also shows oscillations on the output side, for this reason proposed MPPT algorithm is used in this paper. For eliminating oscillations, this algorithm has proven to be better compared to perturb and observe (P&O) and particle swarm optimization (PSO). The results are compared in terms of output power.

A cat in paradise: hunting and feeding behaviour of Eurasian lynx among abundant naive prey

Kill rates of predators typically increase when they come into contact with naive and abundant prey. Such a situation can lead to surplus killing or the occurrence of parallel kills (i.e. additional kills that predator makes while still consuming the carcass from the previous kill). However, there is limited information on the feeding behaviour of predators during such events and how they affect kill rates. Here we report on hunting and feeding behaviour of a male Eurasian lynx (Lynx lynx) that dispersed into a region where this apex predator had been absent for several decades. We also report on the kleptoparasitism by wild boar (Sus scrofa), which effects on lynx prey consumption have not yet been explored. We found 66 ungulates killed by the lynx, among which 39% were part of parallel kills. Compared to the single kills, lynx fed on parallel kills for 2.7-times longer, while the kill rate was 37% higher, resulting in one of the highest kill rates reported so far for male lynx in Europe. We did not detect differences in search times following single or parallel kills and the average distance between consecutive kills was similar in both kill types. We also recorded the highest kleptoparasitism rate by dominant scavengers on Eurasian lynx, as 48% of kills were usurped and consumed by the wild boars. Kleptoparasitism reduced the average time lynx was able to feed on prey for 52% compared to kills not found by wild boars. However, the lynx did not compensate for these losses by increasing the hunting effort, probably due to abundant naive prey available in the area.

Intentional partial beaching in a coral reef fish: a newly recorded hunting behaviour of titan triggerfish, Balistoides viridescens.

Coral reef fishes use a multitude of diverse feeding behaviours to increase their ability to successfully capture a wide range of prey. Here, this study reports a novel hunting behaviour in a coral reef fish, the titan triggerfish, Balistoides viridescens, where an individual was seen partially beaching itself while attempting to catch a Red Sea ghost crab, Ocypode saratan. This is the first report of this behaviour in the order Tetraodontiformes and represents an astonishing capability of this species to exploit food resources outside their typical assumed ecological niche.

Performance and evaluation of energy optimization techniques for wireless body area networks

BackgroundWireless body area networks are created to retrieve and transmit human health information by using sensors on the human body. Energy efficiency is considered a foremost challenge to increase the lifetime of a network. To deal with energy efficiency, one of the important mechanisms is selecting the relay node, which can be modeled as an optimization problem. These days nature-inspired algorithms are being widely used to solve various optimization problems. With regard to this, this paper aims to compare the performance of the three most recent nature-inspired metaheuristic algorithms for solving the relay node selection problem.ResultsIt has been found that the total energy consumption calculated using grey wolf optimization decreased by 23% as compared to particle swarm optimization and 16% compared to ant lion optimization.ConclusionsThe results suggest that grey wolf optimization is better than the other two techniques due to its social hierarchy and hunting behavior. The findings showed that, compared to well-known heuristics such as particle swarm optimization and ant lion optimization, grey wolf optimization was able to deliver extremely competitive results.Graphical