Individual Wolves Research Articles

An Elite Wolf Pack Algorithm Based on the Probability Threshold for a Multi-UAV Cooperative Reconnaissance Mission

In the task assignment problem of multi-UAV collaborative reconnaissance, existing algorithms have issues with inadequate solution accuracy, specifically manifested as large spatial spans and knots of routes in the task execution of UAVs. To address the above challenges, this paper presents a multi-UAV task assignment model under complex conditions (MTAMCC). To efficiently solve this model, this paper proposes an elite wolf pack algorithm based on probability threshold (EWPA-PT). The EWPA-PT algorithm combines the wandering behavior in the traditional wolf pack algorithm with the genetic algorithm. It introduces an ordered permutation problem to calculate the adaptive wandering times of the detective wolves in a specific direction. During the calling phase of the algorithm, the fierce wolves in the wolf pack randomly learn the task assignment results of the head wolf. The sieging behavior introduces the Metropolis criterion from the simulated annealing algorithm to replace the distance threshold in traditional wolf pack algorithms with a probability threshold, which dynamically changes during the iteration process. The wolf pack updating mechanism leverages the task assignment experience of the elite group to reconstruct individual wolves, thereby improving the individual reconstruction’s efficiency. Experiments demonstrate that the EWPA-PT algorithm significantly improves solution accuracy compared to typical methods in recent years.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

The origin, connectivity, and individual specialization of island wolves after deer extirpation.

Wolves are assumed to be ungulate obligates, however, a recently described pack on Pleasant Island, Alaska USA, is persisting on sea otters and other marine resources without ungulate prey, violating this long-held assumption. We address questions about these wolves regarding their origin and fate, degree of isolation, risk of inbreeding depression, and diet specialization by individual and sex. We applied DNA metabarcoding and genotyping by amplicon sequencing using 957 scats collected from 2016 to 2022, and reduced representation sequencing of tissue samples to establish a detailed understanding of Pleasant Island wolf ecology and compare them with adjacent mainland wolves. Dietary overlap was higher among individual wolves on Pleasant Island (Pianka's index mean 0.95 ± 0.03) compared to mainland wolves (0.70 ± 0.21). The individual diets of island wolves were dominated by sea otter, ranging from 40.6% to 63.2% weighted percent of occurrence (wPOO) (mean 55.5 ± 8.7). In contrast, individual mainland wolves primarily fed on ungulates (42.2 ± 21.3) or voles during a population outbreak (31.2 ± 23.2). We traced the origin of the Pleasant Island pack to a mainland pair that colonized around 2013 and produced several litters. After this breeding pair was killed, their female offspring and an immigrant male became the new breeders in 2019. We detected 20 individuals of which 8 (40%) were trapped and killed while two died of natural causes during the 6-year study. Except for the new breeding male, the pedigree analysis and genotype results showed no additional movement to or from the island, indicating limited dispersal but no evidence of inbreeding. Our findings suggest wolves exhibit more flexible foraging behavior than previously believed, and hunting strategies can substantially differ between individuals within or between packs. Nevertheless, anthropogenic and natural mortality combined with limited connectivity to the mainland may inhibit the continued persistence of Pleasant Island wolves.

A novel lifting point location optimization method of transmission line tower based on improved grey wolf optimizer

In the erection process of transmission line tower, the appropriate lifting point position is an important factor in ensuring the stability and balance of the lifting process and preventing deformation and damage to the towers. In this paper, a improved grey wolf optimization algorithm is proposed to solve the issues of low optimization efficiency and easily getting trapped in local minima when optimizing the lifting point position of transmission line towers. The improved algorithm includes the use of a good point-set strategy to enhance the initialization method of grey wolf individuals, ensuring a more uniform distribution of the population and reducing ineffective searches in the early stages of optimization. Furthermore, two random operators are utilized to combine and mutate the optimal grey wolf position, thereby enhancing the algorithm's ability to escape local optima. Finally, the trend information of the optimization process is considered, and the median value of the population is used to improve the stability of the optimization algorithm. Experimental results demonstrate that the proposed algorithm has better optimization performance and faster convergence speed compared to genetic algorithm, particle swarm optimization algorithm, and artificial fish swarm algorithm. It effectively addresses the optimization problem of lifting point position for transmission line towers.

Cuckoo Coupled Improved Grey Wolf Algorithm for PID Parameter Tuning

In today’s automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a locally optimal solution. First, the tent chaotic mapping method is used to initialise the population instead of using random initialization to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population. Finally, inspired by the cuckoo search optimisation (CSO) algorithm, the Levy flight strategy is introduced to update the position equation so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions. Then, comparing this method with the other two improved grey wolf algorithms, it can be seen that this method increases the average and standard deviation by an order of magnitude and effectively improves the global optimal search ability and convergence speed. Finally, in the experimental section, three parameter tuning methods were compared from four aspects: overshoot, steady-state time, rise time, and steady-state error, using the ball screw motor as the control object. In terms of overall dynamic performance, the method proposed in this article is superior to the other three parameter tuning methods.

Edge Detection Algorithm for Infrared Images of Power Equipment Based on Improved Grey Wolf Algorithm and Corrosion Algorithm

The enhanced grey wolf algorithm is merged with the corrosion algorithm, and a grey wolf corrosion optimization algorithm is proposed to precisely identify the boundaries within infrared images of power equipment. The refined grey wolf algorithm employs exponential entropy as its fitness function and incorporates an individual wolf selection mechanism, enabling binary segmentation with adaptively chosen thresholds and high resemblance to the original image. Simulation results of actual infrared images captured in substations reveal that, compared to conventional edge detection algorithms such as Sobel, Roberts, Prewitt, Log, and Canny, the algorithm proposed in this paper yields comprehensive and well-defined boundaries with a high degree of conformity to the original infrared image. This proposed algorithm demonstrates significant applicability in the domain of infrared image processing and target recognition for power equipment.

Employee Attrition Prediction based on Grey Wolf Optimization and Deep Neural Networks

Despite the constructive application of promising technologies such as Neural Networks, their potential for predicting human resource management outcomes still needs to be explored. Therefore, the primary aim of this paper is to utilize neural networks and meta-heuristic technologies to predict employee attrition, thereby enhancing prediction model performance. The conventional Grey Wolf optimization optimization (GWO) has gained substantial attention notice because of its attributes of robust convergence, minimal parameters, and simple implementaton. However, it encounter problems with slow convergence rates and susceptibility to local optima in practical optimization scenarios. To address these problems, this paper introduces an enhanced Grey Wolf Optimization algorithm incorporating the utilization of Cauchy-Gaussian mutation, which contributes to enhancing diversity within the leader wolf population and enhances the algorithm's global search capabilities. Additionally, this work preserves exceptional grey wolf individuals through a greedy selection of 2 mechanisms to ensure accelerated convergence. Moreover, an enhanced exploration strategy is suggested to expand the optimization possibilities of the algorithm and improve its convergence speed. The results shows that the proposed model achieved the accuarcy of 97.85%, precision of 98.45%, recall of 98.14%, and f1-score of 97.11%. Nevertheless, this paper extends its scope beyond merely predicting employee attrition probability and activities to enhance the precision of such predictions by constructing an improved model employing a Deep Neural Network (DNN)..

Global path planning for airport energy station inspection robots based on improved grey wolf optimization algorithm

Global path planning is one of the key technologies for airport energy station inspection robots to achieve autonomous navigation. Due to the complexity of airport energy station buildings with numerous mechanical and electrical equipment and narrow areas, planning an optimal global path remains a challenge. This paper aimed to study global path planning for airport energy station inspection robots using an improved version of the Grey Wolf Optimizer (IGWO) algorithm. Firstly, the initialization process of the Grey Wolf Optimizer algorithm selects several grey wolf individuals closer to the optimal solution as the initial population through the lens imaging reverse learning strategy. The algorithm introduces nonlinear convergence factors in the control parameters, and adds an adaptive adjustment strategy and an elite individual reselection strategy to the location update to improve the search capability and to avoid falling into local optima. Benchmark function and global path planning simulation experiments were carried out in MATLAB to test the proposed algorithm’s effectiveness. The results showed that compared to other swarm intelligent optimization algorithms, the proposed algorithm outperforms them in terms of higher convergence speed and optimization accuracy. Friedman’s test ranked this algorithm first overall. The algorithm outperforms others in terms of average path length, standard deviation of path length, and running time.

Agricultural UAV trajectory planning by incorporating multi-mechanism improved grey wolf optimization algorithm

Unmanned aerial vehicle (UAV) trajectory planning plays an essential role in agricultural production and biological control. To solve the agricultural UAV trajectory planning problem, a multi-mechanism collaborative improved grey wolf optimization algorithm (NAS-GWO) is proposed. In NAS-GWO, the evolutionary boundary constraint processing mechanism is introduced to update the position of the grey wolf individuals that cross the boundary in time to retain the position information of the optimal individuals to the largest degree to enhance the search accuracy of the algorithm. Then, the Gaussian mutation strategy and spiral function are used as perturbation mechanisms to help the algorithm jump out of the local optimum in time to strengthen the exploitation capability of NAS-GWO. Meanwhile, the improved Sigmoid function is used as a nonlinear convergence factor for balancing the exploitation and exploration of the NAS-GWO. By comparing NAS-GWO with ten advanced metaheuristic algorithms on 20 CEC2017 benchmark functions, the experimental results show that the NAS-GWO algorithm has superior merit seeking and robustness. Moreover, the agricultural UAV trajectory planning problem is solved using NAS-GWO. The experimental results show that the NAS-GWO algorithm plans a more viable and stable trajectory path in four different scale missions, while the most important is that it requires less cost. Among them, the algorithm reduces 27.93%, 38.15%, 32.32%, 34.11%, 10.63%, and 13.48% on average in cost function values compared to Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Aquila Optimizer (AO), Differential Evolution (DE), Dung Beetle Optimizer (DBO), and Grey Wolf Optimization algorithm (GWO), thus proving the effectiveness and significance of NAS-GWO in the agricultural UAV trajectory path planning problem.

Estimating wolf (Canis lupus) densities using video camera traps and spatial capture–recapture analysis

AbstractEstimating population density is critical for effective species conservation, wildlife management planning, and long‐term monitoring. Obtaining accurate estimates is especially important for the wolf (Canis lupus), a widely distributed northern hemisphere apex predator whose management and conservation are highly controversial in most of its range, and whose presence usually generates high‐profile media coverage. The peculiarities of wolf social spatial organization and behavior can violate the assumptions of capture–recapture models (uniformity and independence, respectively) to a greater or lesser extent and make it difficult to obtain precise and reliable density estimates. This paper presents a case study, which estimated the population density of the Iberian wolf in the Dorsal Gallega mountain ridge (Galicia, NW Spain) based on the identification of individual wolves from their traits and behavior using video camera traps and spatially explicit capture–recapture (SCR) analyses. The study followed three phases. Firstly, field data were collected by installing camera traps and changing their location until the entire area was sampled. Second, a complete morphological and behavioral study of the wolves recorded was performed to facilitate individual recognition. Third, overdispersion due to gregariousness and other sources of heterogeneity was modeled in the SCR analyses comparing Poisson and negative binomial observation models with different random effects on the baseline detection probability. We estimated a density of 2.88 (SD: 0.37) wolves/100 km2 in the study area. We concluded that estimating wolf population size using camera trap videos, individual identification, and SCR provides a feasible method and can be used for estimating the density in similar species.

Infrastructure as biopolitics: Fencing, categorizing and valuing animals for wolf conservation in Germany

This paper takes a critical look at biopolitical practices under the current regime for wolf conservation in Germany. One profound difference between human and more-than-human biopolitics is that it concerns multiple, more-than-human bodies, demanding a close examination of governance practices aimed at enabling conservation. My core argument is twofold: first, I argue that conservation policies in Germany attempt to frame wolves as collectivities and apply a population perspective on these animals. However, the very technologies that aim to monitor wolves as population, namely genetic analyses, also produce individual wolves that complicate conservation efforts due to their affective force (Lorimer, 2015). Second, as active wolf management in the form of population control is rare due to legal regulations, there is a strong focus on livestock protection measures. These infrastructuring practices (Barua, 2021) continue to fail because they rest on the idea that convivial coexistence can be quantified and ignore that technologies such as fences can alter animals’ atmospheres (Lorimer et al., 2019) profoundly. As a result, infrastructuring practices aimed at enabling coexistence may run counter to their initial purpose. With fences becoming habitats, lively and discursive multispecies relations are renegotiated.

Characteristics of reproductive organs and reproductive potential in Scandinavian female grey wolves (Canis lupus).

The Swedish wolf population is closely monitored and managed to keep the population at a sustainable level while avoiding conflicts. Detailed knowledge about reproduction is crucial for estimates of population size and the reproductive potential of a population. Post-mortem evaluation of reproductive organs can be used as a complementary tool to field monitoring for evaluation of cyclicity and previous pregnancy, including litter size. Therefore, we evaluated reproductive organs from 154 female wolves that were necropsied during the period 2007–2018. The reproductive organs were weighed, measured, and inspected according to a standardised protocol. Presence of placental scars was evaluated for estimates of previous pregnancy and litter size. Data about individual wolves were also obtained from national carnivore databases. Body weight increased during the first year of life before levelling out. There was evidence of cyclicity the first season after birth in 16.3 % of the 1-year-old females. No females < 2 years had evidence of a previous pregnancy. Pregnancy rates were significantly lower in 2- and 3-year old females than in older females. Mean uterine litter size was 4.9 ± 2.3, and did not differ significantly between age groups. Our data supports earlier field data that female wolves usually start to reproduce at the earliest at 2-years of age but that they occasionally start to cycle one season earlier. All females ≥ 4 years of age had reproduced. Pathological findings of the reproductive organs were rare, indicating that reproductive health of female wolves is not a limiting factor for population growth.

Behavior automatic analysis for wolf pack hunting: making fast behavior analysis of massive data possible

Automatic wolf pack hunting behavior analysis contributes to the identification of behavioral mechanism and the development of bio-swarm intelligence engineering. However, wolf pack hunting commonly involves a complicated background and high-speed motion, where the frequent interactions with each other lead to frequent visual occlusion of the individual wolves. These difficulties make the automatic analysis of group hunting behavior significantly more challenging. Hence, we develop an automatic wolf pack hunting behavior detection scheme appropriate for videos, comprising a four-level individual feature map (frame, video, space, and semantic) and a group feature map. We propose a temporal–spatial feature fusion-based motion state recognition algorithm appropriate for scale-varied and occlusion-frequent individuals to obtain a robust semantic-level feature. Based on this individual feature map, we propose a video-based wolf pack hunting automatic behavior detection method. The developed scheme is validated on our Wolf2022 dataset, while the proposed motion state recognition and group behavior detection algorithms are further tested through ablation experiments. The results revealed that the motion state recognition accuracy reaches 88%, correctly detecting 15 out of 17 group behavior video clips.Significance statementIt is difficult to fast extract quantitative analysis results of wolf pack hunting behavior from video data. Our research focused on the research of the automatic analysis method for wolf pack hunting. Using the proposed method, the statical individual and group behavior attributes can be automatically generated from video data, which contributes to the building of a conceptual bridge between the wolf pack hunting behavior and bio-swarm intelligence engineering.

Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection

The traditional grey wolf algorithm is widely used for feature selection. However, within complex feature multi-dimensional problems, the grey wolf algorithm is prone to reach locally optimal solutions and premature convergence. In this paper, a threshold binary grey wolf optimizer based on multi-elite interaction for feature selection (MTBGWO) is proposed. Firstly, the multi-population topology is adopted to enhance the population’s diversity for improving search space utilization. Secondly, an information interaction learning strategy is adopted for the update of sub-population elite wolf position (optimal position) via learning better position from other elite wolves; in order to improve the local exploitation ability of the sub-population. At the same time, the command of β and δ wolves (second and third best positions) for population position updates is removed. Finally, a threshold approach is employed to convert the continuous position of grey wolf individuals into binary one to apply in the feature selection problem. Further, The MTBGWO algorithm proposed in this paper is compared with the traditional binary grey wolf algorithm (BGWO), binary whale algorithm (BWOA), as well as some recently developed novel algorithms to exhibit its superiority and robustness. Totally 16 classification datasets, from the UCI Machine Learning Repository, are chosen for comparison. TheWilcoxon’s rank-sum non-parametric statistical test is carried out at 5% significance level to evaluate whether the results of the proposed algorithms significantly differs from those of the other algorithms. In the experimental results for all datasets, the overall average accuracy of the MTBGWO algorithm is 94.7%, while the highest of the other algorithms is 92.8% and the selected feature subset is 25% of the total dataset, while the GWO andWOA algorithms are twice as high as MTBGWO. In terms of computational efficiency, the overall processing time of MTBGWO is 24.2 seconds, whereas HSGW is 44.1 seconds. The results reveal that the MTBGWO has shown its superiority in solving the feature selection problem.

Camera trapping as a method for estimating abundance of Mexican wolves

AbstractEstimating wildlife abundance, particularly for rare and elusive species, is challenging because of time, cost, and methodological constraints. The Mexican wolf (Canis lupus baileyi), a federally‐listed endangered subspecies of gray wolf, is currently monitored using ground and aerial methods to obtain a minimum known population count. As the Mexican wolf population has grown and expanded, the time and cost required to monitor the subspecies has increased. We investigated the efficacy of camera trapping for estimating Mexican wolf abundance by comparing the accuracy, precision, and cost of camera trapping to those obtained with current monitoring techniques. Between 1 November 2019 and 31 July 2020, we collected 13,317 photos of wolves from 124 camera traps in Arizona where Mexican wolves were known to occur, excluding tribal lands. We used a spatial mark‐resight analysis to estimate abundance for both winter (November 2019 through February 2020) and summer (April through July 2020) seasons, with and without the assistance of global positioning system (GPS) telemetry data to identify individual wolves. Combined with GPS data, camera trapping provided a summer abundance estimate ( = 50, 95% CI = 37–64) that was 14% lower than the 2019 minimum known population count (N = 59), but included the minimum known population count in the 95% confidence interval. The summer no telemetry abundance estimate was 27% below the minimum known population count ( = 43, 95% CI = 30–56). During winter, abundance estimates obtained from camera trapping (no telemetry: = 33, 95% CI = 15–52; telemetry: = 45, 95% CI = 28–62), were much lower than the 2019 Mexican wolf minimum known population count (winter: N = 62), but included the minimum known population count in the 95% confidence interval for the winter telemetry dataset. A cost comparison indicated that the first‐year camera trapping equipment expenses were 1.7 times the equipment cost of the current method and that camera trapping equipment expenses in subsequent years were equivalent to the equipment cost of the current method, and required at least 1.4 times the labor hours. We provide recommendations to potentially improve Mexican wolf abundance estimates and minimize camera trapping expenses. The results of our project may help managers make appropriate decisions for their population monitoring needs, while considering budget, staffing capabilities, precision, and accuracy.

Evaluating noninvasive methods for estimating cestode prevalence in a wild carnivore population.

Helminth infections are cryptic and can be difficult to study in wildlife species. Helminth research in wildlife hosts has historically required invasive animal handling and necropsy, while results from noninvasive parasite research, like scat analysis, may not be possible at the helminth species or individual host levels. To increase the utility of noninvasive sampling, individual hosts can be identified by applying molecular methods. This allows for longitudinal sampling of known hosts and can be paired with individual-level covariates. Here we evaluate a combination of methods and existing long-term monitoring data to identify patterns of cestode infections in gray wolves in Yellowstone National Park. Our goals were: (1) Identify the species and apparent prevalence of cestodes infecting Yellowstone wolves; (2) Assess the relationships between wolf biological and social characteristics and cestode infections; (3) Examine how wolf samples were affected by environmental conditions with respect to the success of individual genotyping. We collected over 200 wolf scats from 2018-2020 and conducted laboratory analyses including individual wolf genotyping, sex identification, cestode identification, and fecal glucocorticoid measurements. Wolf genotyping success rate was 45%, which was higher in the winter but decreased with higher precipitation and as more time elapsed between scat deposit and collection. One cestode species was detected in 28% of all fecal samples, and 38% of known individuals. The most common infection was Echinococcus granulosus sensu lato (primarily E. canadensis). Adult wolves had 4x greater odds of having a cestode infection than pups, as well as wolves sampled in the winter. Our methods provide an alternative approach to estimate cestode prevalence and to linking parasites to known individuals in a wild host system, but may be most useful when employed in existing study systems and when field collections are designed to minimize the time between fecal deposition and collection.

Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy

The traditional Grey Wolf Optimization algorithm (GWO) has received widespread attention due to features of strong convergence performance, few parameters, and easy implementation. However, in actual optimization projects, there are problems of slow convergence speed and easy to fall into local optimal solution. The paper proposed a Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy (CG-GWO) in response to the above problems. The Cauchy-Gaussian mutation operator is introduced to increase the population diversity of the leader wolves and improve the global search ability of the algorithm. This work retains outstanding grey wolf individuals through the greedy selection mechanism to ensure the convergence speed of the algorithm. An improved search strategy was proposed to expand the optimization space of the algorithm and improve the convergence accuracy. Experiments are performed with 16 benchmark functions covering unimodal functions, multimodal functions, and fixed-dimension multimodal functions to verify the effectiveness of the algorithm. Experimental results show that compared with four classic optimization algorithms, particle swarm optimization algorithm (PSO), whale optimization algorithm (WOA), sparrow optimization algorithm (SSA), and farmland fertility algorithm (FFA), the CG-GWO algorithm shows better convergence accuracy, convergence speed, and global search ability. The proposed algorithm shows the same better performance compared with a series of improved algorithms such as the improved grey wolf algorithm (IGWO), modified Grey Wolf Optimization algorithm (mGWO), and the Grey Wolf Optimization algorithm inspired by enhanced leadership (GLF-GWO).

Wolf Pack's Role Matching Labor Division Model for Dynamic Task Allocation of Swarm Robotics

First, through in-depth analysis of the diversified collective behaviors in wolf pack, this study summarizes four remarkable features of wolf pack's labor division. Second, the wolf pack's role-task matching labor division mechanism is investigated, namely the individual wolves perform specific tasks that match their respective roles, and then a novel role matching labor division model is proposed. Finally, the performances of RMM are tested and evaluated with two swarm robotics task allocation scenarios. It is proved that RMM has higher solving efficiency and faster calculation speed for the concerned problem than the compared approach. Moreover, the proposed model shows advantages in the task allocation balance, robustness, and real time, especially in the dynamic response capability to the complex and changing environments.

Bioacoustic Detection of Wolves: Identifying Subspecies and Individuals by Howls.

Wolves (Canis lupus) are generally monitored by visual observations, camera traps, and DNA traces. In this study, we evaluated acoustic monitoring of wolf howls as a method for monitoring wolves, which may permit detection of wolves across longer distances than that permitted by camera traps. We analyzed acoustic data of wolves' howls collected from both wild and captive ones. The analysis focused on individual and subspecies recognition. Furthermore, we aimed to determine the usefulness of acoustic monitoring in the field given the limited data for Eurasian wolves. We analyzed 170 howls from 16 individual wolves from 3 subspecies: Arctic (Canis lupus arctos), Eurasian (C. l. lupus), and Northwestern wolves (C. l. occidentalis). Variables from the fundamental frequency (f0) (lowest frequency band of a sound signal) were extracted and used in discriminant analysis, classification matrix, and pairwise post-hoc Hotelling test. The results indicated that Arctic and Eurasian wolves had subspecies identifiable calls, while Northwestern wolves did not, though this sample size was small. Identification on an individual level was successful for all subspecies. Individuals were correctly classified with 80%-100% accuracy, using discriminant function analysis. Our findings suggest acoustic monitoring could be a valuable and cost-effective tool that complements camera traps, by improving long-distance detection of wolves.

Occurrence and Livestock Depredation Patterns by Wolves in Highly Cultivated Landscapes

Attacks by large predators on livestock are an important driver of conflicts. Consequently, knowledge about where predators occur, where livestock depredation takes place and what factors influence it will aid the mitigation of stakeholder conflicts. Following legal protection, wolves (Canis lupus) in Central Europe are recently spreading to areas dominated by agriculture, bringing them in closer contact with livestock. Here, we analyzed habitat selection and livestock depredation rates of 43 wolves identified by genotyping on the Jutland peninsula, consisting of mainland Denmark and the northernmost German federal state Schleswig-Holstein. Occupancy by resident wolves correlated positively with forest and other non-forested semi-natural land cover (habitat for natural ungulate prey), whereas occupancy by non-resident wolves correlated with increasing forest cover and sheep density. The latter effect likely reflected increased sampling probability of highly mobile dispersers killing livestock. We recorded 565 livestock depredation events (85 in Denmark and 480 in Schleswig-Holstein), of which 42% (55 in DK and 185 in SH) could be assigned to 27 individual wolves based on DNA evidence. Livestock (mostly sheep) were killed by wolves in 16% of the study area. Our results indicate that wolves mostly killed livestock as a context-dependent response, i.e., being dispersers in agricultural areas with low availability of wild ungulate prey and high livestock densities, and not because of behavioral preferences for sheep. Moreover, the livestock depredation was lower in areas with livestock protection measures (implemented in areas with established pairs/packs). We conclude that while wolf attacks on livestock in established wolf territories generally can be reduced through improvement of fences, livestock depredation by non-resident wolves in agricultural areas constitutes a bigger challenge. Albeit technically possible, the economic costs of implementing predator-proof fences and other preventive measures in such pastoral areas infrequently visited by wolves will be considerable. Experiences so far further indicate that lethal removal of identified “problem wolves” may be inefficient in practice.