Wolves make roadways safer, generating large economic returns to predator conservation.

Recent studies uncover cascading ecological effects resulting from removing and reintroducing predators into a landscape, but little is known about effects on human lives and property. We quantify the effects of restoring wolf populations by evaluating their influence on deer-vehicle collisions (DVCs) in Wisconsin. We show that, for the average county, wolf entry reduced DVCs by 24%, yielding an economic benefit that is 63 times greater than the costs of verified wolf predation on livestock. Most of the reduction is due to a behavioral response of deer to wolves rather than through a deer population decline from wolf predation. This finding supports ecological research emphasizing the role of predators in creating a "landscape of fear." It suggests wolves control economic damages from overabundant deer in ways that human deer hunters cannot.

Indoor Infrared Sensor Layout Optimization for Elderly Monitoring Based on Fused Genetic Gray Wolf Optimization (FGGWO) Algorithm.

With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic Algorithm (GA) and the local search capability of Gray Wolf Optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system's adaptive and real-time response capabilities.

Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling.

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér-Rao lower bound (CRLB), even at low SNR levels.

Classification of breast tumors by using a novel approach based on deep learning methods and feature selection.

PURPOSE: Cancer is one of the most insidious diseases that the most important factor in overcoming the cancer is early diagnosis and detection. The histo-pathological images are used to determine whether the tissue is cancerous and the type of cancer. As the result of examination on tissue images by the expert personnel, the cancer type, and stage of the tissue can be determined. However, this situation can cause both time and energy loss as well as personnel-related inspection errors. By the increased usage of computer-based decision methods in the last decades, it would be more efficient and accurate to detect and classify the cancerous tissues with computer-aided systems. METHODS: As classical image processing methods were used for cancer-type detection in early studies, advanced deep learning methods based on recurrent neural networks and convolutional neural networks have been used more recently. In this paper, popular deep learning methods such as ResNet-50, GoogLeNet, InceptionV3, and MobilNetV2 are employed by implementing novel feature selection method in order to classify cancer type on a local binary class dataset and multi-class BACH dataset. RESULTS: The classification performance of the proposed feature selection implemented deep learning methods follows as for the local binary class dataset 98.89% and 92.17% for BACH dataset which is much better than most of the obtained results in literature. CONCLUSION: The obtained findings on both datasets indicates that the proposed methods can detect and classify the cancerous type of a tissue with high accuracy and efficiency.

Optimized preparation of gangue waste-based geopolymer adsorbent based on improved response surface methodology for Cd(II) removal from wastewater.

Resource utilization of gangue solid waste has become an essential research direction for green development. This study prepared a novel gangue based geopolymer adsorbent (GPA) for the removal of Cd(II) from wastewater using pretreatment gangue (PG) as the main raw material. The ANOVA indicated that the obtained quadratic model of fitness function (R2 > 0.99, P-value <0.0001) was significant and adequate, and the contribution of the three preparation conditions to the removal of Cd(II) was: calcination temperature > Na2CO3:PG ratio > water-glass solid content. The hybrid response surface method and gray wolf optimization (RSM-GWO) algorithm were adopted to acquire the optimum conditions: Na2CO3:PG ratio = 1.05, calcination temperature of 701 °C, solid content of water glass of 22.42%, and the removal efficiency of Cd(II) by GPA obtained under the optimized conditions (GPAC) was 97.84%. Adsorption kinetics, adsorption isotherms and characterization by XRD, FTIR, Zeta potential, FSEM-EDS and BET were utilized to investigate the adsorption mechanism of GPAC on Cd(II). The results showed that the adsorption of Cd(II) from GPAC was consistent with the pseudo-second-order model (R2 = 0.9936) and the Langmuir model (R2 = 0.9988), the adsorption was a monolayer adsorption process and the computed maximum Cd(II) adsorption (50.76 mg g-1) was approximate to experimental results (51.47 mg g-1). Moreover, the surface morphology of GPAC was rough and porous with a specific surface area (SSA) of 18.54 m2 g-1, which provided abundant active sites, and the internal kaolinite was destroyed to produce a zeolite-like structure where surface complexation and ion exchange with Cd(II) through hydroxyl (-OH) and oxygen-containing groups (-SiOH and -AlOH) were the main adsorption mechanisms. Thus, GPAC is a lucrative adsorbent material for effective Cd(II) wastewater treatment, complying with the "high value-added" usage of solid wastes and "waste to cure poison" green sustainable development direction.

Genomic analysis of wolves from Pakistan clarifies boundaries among three divergent wolf lineages.

Among the three main divergent lineages of gray wolf (Canis lupus), the Holarctic lineage is the most widespread and best-studied, particularly in North America and Europe. Less is known about Tibetan (also called Himalayan) and Indian wolf lineages in southern Asia, especially in areas surrounding Pakistan where all three lineages are thought to meet. Given the endangered status of the Indian wolf in neighboring India and unclear southwestern boundary of the Tibetan wolf range, we conducted mitochondrial and genome-wide sequencing of wolves from Pakistan and Kyrgyzstan. Sequences of the mitochondrial D-loop region of 81 wolves from Pakistan indicated contact zones between Holarctic and Indian lineages across the northern and western mountains of Pakistan. Reduced-representation genome sequencing of 8 wolves indicated an east-to-west cline of Indian to Holarctic ancestry, consistent with a contact zone between these two lineages in Pakistan. The western boundary of the Tibetan lineage corresponded to the Ladakh region of India's Himalayas with a narrow zone of admixture spanning this boundary from the Karakoram Mountains of northern Pakistan into Ladakh, India. Our results highlight the conservation significance of Pakistan's wolf populations, especially the remaining populations in Sindh and Southern Punjab that represent the highly endangered Indian lineage.

Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM.

The wind tunnel balance signal detection system is widely employed in aerospace applications for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature compensation method based on an improved gray wolf optimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial population position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.

Smart Parking Locks Based on Extended UNET-GWO-SVM Algorithm.

Due to the rapid increase in private car ownership in China, most cities face the problem of insufficient parking spaces, leading to frequent occurrences of parking space conflicts. There is a wide variety of parking locks available on the market. However, most of them lack advanced intelligence and cannot cater to the growing diverse needs of people. The present study attempts to devise a smart parking lock to tackle this issue. Specifically, the smart parking lock uses a Raspberry Pi as the core controller, senses the vehicle with an ultrasonic ranging module, and collects the license plate image with a camera. In addition, algorithms for license plate recognition based on traditional image-processing methods typically require a high pixel resolution, but their recognition accuracy is often low. Therefore, we propose a new algorithm called UNET-GWO-SVM to achieve higher accuracy in embedded systems. Moreover, we developed a WeChat mini program to control the smart parking lock. Field tests were conducted on campus to evaluate the performance of the parking locks. The test results show that the corresponding effective unlocking rate is 99.0% when the recognition error is less than two license plate characters. The average time consumption is controlled at about 2 s. It can meet real-time requirements.

An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization.

Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities and can cause delays in the optimal operation and management tasks. Therefore, it is clear that a fast and reliable architecture is needed to make big data management in SGs more efficient. This paper assesses the optimal operation of the SGs using cloud computing (CC), fog computing, and resource allocation to enhance the management problem. Technically, big data management makes SG more efficient if cloud and fog computing (CFC) are integrated. The integration of fog computing (FC) with CC minimizes cloud burden and maximizes resource allocation. There are three key features for the proposed fog layer: awareness of position, short latency, and mobility. Moreover, a CFC-driven framework is proposed to manage data among different agents. In order to make the system more efficient, FC allocates virtual machines (VMs) according to load-balancing techniques. In addition, the present study proposes a hybrid gray wolf differential evolution optimization algorithm (HGWDE) that brings gray wolf optimization (GWO) and improved differential evolution (IDE) together. Simulation results conducted in MATLAB verify the efficiency of the suggested algorithm according to the high data transaction and computational time. According to the results, the response time of HGWDE is 54 ms, 82.1 ms, and 81.6 ms faster than particle swarm optimization (PSO), differential evolution (DE), and GWO. HGWDE's processing time is 53 ms, 81.2 ms, and 80.6 ms faster than PSO, DE, and GWO. Although GWO is a bit more efficient than HGWDE, the difference is not very significant.

Aleppo pine seeds (Pinus halepensis Mill.) as a promising novel green coagulant for the removal of Congo red dye: Optimization via machine learning algorithm.

Consideration is now being given to the use of metal coagulants to remove turbidity from drinking water and wastewater. Concerns about the long-term impact of non-biodegradable sludge on human health and the potential contamination of aquatic systems are gaining popularity. Recently, alternative biocoagulants have been suggested to address these concerns. In this study, using a 1 M sodium chloride (NaCl) solution, the active coagulating agent was extracted from Pinus halepensis Mill. Seed, and used for the first time to remove Congo red dye, the influence of numerous factors on dye removal was evaluated in order to make comparisons with conventional coagulants. The application of biocoagulant was shown to be very successful, with coagulant dosages ranging from 3 to 12 mL L-1 achieving up to 80% dye removal and yielding 28 mL L-1 of sludge. It was also found that biocoagulant is extremely pH sensitive with an optimum operating pH of 3. Ferric chloride, on the other hand, achieved similar removal rate with higher sludge production (46 mL L-1) under the same conditions. A Fourier Transform Infrared Spectroscopy and proximate composition analysis were undertaken to determine qualitatively the potential active coagulant ingredient in the seeds and suggested the involvement of proteins in the coagulation-flocculation mechanism. The evaluation criteria of the Support vector machine_Gray wolf optimizer model in terms of statistical coefficients and errors reveals quite interesting results and demonstrates the performance of the model, with statistical coefficients close to 1 (R = 0.9998, R2 = 0.9995 and R2 adj = 0.9995) and minimal statistical errors (RMSE = 0.5813, MSE = 0.3379, EPM = 0 0.9808, ESP = 0.9677 and MAE = 0.2382). The study findings demonstrate that Pinus halepensis Mill. Seed extract might be a novel, environmentally friendly, and easily available coagulant for water and wastewater treatment.

GWLBC: Gray Wolf Optimization Based Load Balanced Clustering for Sustainable WSNs in Smart City Environment.

In a smart city environment, with increased demand for energy efficiency, information exchange and communication through wireless sensor networks (WSNs) plays an important role. In WSNs, the sensors are usually operating in clusters, and they are allowed to restructure for effective communication over a large area and for a long time. In this scenario, load-balanced clustering is the cost-effective means of improving the system performance. Although clustering is a discrete problem, the computational intelligence techniques are more suitable for load balancing and minimizing energy consumption with different operating constraints. The literature reveals that the swarm intelligence-inspired computational approaches give excellent results among population-based meta-heuristic approaches because of their more remarkable exploration ability. Conversely, in this work, load-balanced clustering for sustainable WSNs is presented using improved gray wolf optimization (IGWO). In a smart city environment, the significant parameters of energy-efficient load-balanced clustering involve the network lifetime, dead cluster heads, dead gateways, dead sensor nodes, and energy consumption while ensuring information exchange and communication among the sensors and cluster heads. Therefore, based on the above parameters, the proposed IGWO is compared with the existing GWO and several other techniques. Moreover, the convergence characteristics of the proposed algorithm are demonstrated for an extensive network in a smart city environment, which consists of 500 sensors and 50 cluster heads deployed in an area of 500 × 500 m2, and it was found to be significantly improved.

Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks.

Although IoT technology is advanced, wireless systems are prone to faults and attacks. The replaying information about routing in the case of multi-hop routing has led to the problem of identity deception among nodes. The devastating attacks against the routing protocols as well as harsh network conditions make the situation even worse. Although most of the research in the literature aim at making the IoT system more trustworthy and ensuring faultlessness, it is still a challenging task. Motivated by this, the present proposal introduces a trust-aware routing mechanism (TARM), which uses an edge node with mobility feature that can collect data from faultless nodes. The edge node works based on a trust evaluation method, which segregates the faulty and anomalous nodes from normal nodes. In TARM, a modified gray wolf optimization (GWO) is used for forming the clusters out of the deployed sensor nodes. Once the clusters are formed, each cluster's trust values are calculated, and the edge node starts collecting data only from trustworthy nodes via the respective cluster heads. The artificial bee colony optimization algorithm executes the optimal routing path from the trustworthy nodes to the mobile edge node. The simulations show that the proposed method exhibits around a 58% hike in trustworthiness, ensuring the high security offered by the proposed trust evaluation scheme when validated with other similar approaches. It also shows a detection rate of 96.7% in detecting untrustworthy nodes. Additionally, the accuracy of the proposed method reaches 91.96%, which is recorded to be the highest among the similar latest schemes. The performance of the proposed approach has proved that it has overcome many weaknesses of previous similar techniques with low cost and mitigated complexity.

Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control.

During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line.

IGWO-IVNet3: DL-Based Automatic Diagnosis of Lung Nodules Using an Improved Gray Wolf Optimization and InceptionNet-V3.

Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed to a late stage, making it a leading cause of cancer-related mortality. Lung cancer is fatal if not treated early, making this a significant issue. Initial diagnosis of malignant nodules is often made using chest radiography (X-ray) and computed tomography (CT) scans; nevertheless, the possibility of benign nodules leads to wrong choices. In their first phases, benign and malignant nodules seem very similar. Additionally, radiologists have a hard time viewing and categorizing lung abnormalities. Lung cancer screenings performed by radiologists are often performed with the use of computer-aided diagnostic technologies. Computer scientists have presented many methods for identifying lung cancer in recent years. Low-quality images compromise the segmentation process, rendering traditional lung cancer prediction algorithms inaccurate. This article suggests a highly effective strategy for identifying and categorizing lung cancer. Noise in the pictures was reduced using a weighted filter, and the improved Gray Wolf Optimization method was performed before segmentation with watershed modification and dilation operations. We used InceptionNet-V3 to classify lung cancer into three groups, and it performed well compared to prior studies: 98.96% accuracy, 94.74% specificity, as well as 100% sensitivity.

Behaviorally-mediated trophic cascade attenuated by prey use of risky places at safe times.

The mere threat of predation may incite behavioral changes in prey that lead to community-wide impacts on productivity, biodiversity, and nutrient cycling. The paucity of experimental manipulations, however, has contributed to controversy over the strength of this pathway in wide-ranging vertebrate systems. We investigated whether simulated gray wolf (Canis lupus) presence can induce behaviorally-mediated trophic cascades, specifically, whether the 'fear' of wolf olfactory cues alone can change deer foraging behavior in ways that affect plants and soils. Wolves were recently removed from the Cedar Creek Ecosystem Science Reserve (Minnesota, USA), such that consumptively mediated predator effects were negligible. At 32 experimental plots, we crossed two nested treatments: wolf urine application and herbivore exclosures. We deployed camera traps to quantify how white-tailed deer (Odocoileus virginianus) adjusted their spatiotemporal habitat use, foraging, and vigilance in response to wolf cues and how these behavioral changes affected plant productivity, plant communities, and soil nutrients. Weekly applications of wolf urine significantly altered deer behavior, but deer responses did not cascade to affect plant or soil properties. Deer substantially reduced crepuscular activity at wolf-simulated sites compared to control locations. As wolves in this area predominantly hunted during mornings and evenings, this response potentially allows deer to maximize landscape use by accessing dangerous areas when temporal threat is low. Our experiment suggests that prey may be sensitive to 'dynamic' predation risk that is structured across both space and time and, consequentially, prey use of risky areas during safe times may attenuate behaviorally-mediated trophic cascades at the predator-prey interface.

Estimation of Transfer Function Coefficients for Second-Order Systems via Metaheuristic Algorithms.

The present research develops the parametric estimation of a second-order transfer function in its standard form, employing metaheuristic algorithms. For the estimation, the step response with a known amplitude is used. The main contribution of this research is a general method for obtaining a second-order transfer function for any order stable systems via metaheuristic algorithms. Additionally, the Final Value Theorem is used as a restriction to improve the velocity search. The tests show three advantages in using the method proposed in this work concerning similar research and the exact estimation method. The first advantage is that using the Final Value Theorem accelerates the convergence of the metaheuristic algorithms, reducing the error by up to 10 times in the first iterations. The second advantage is that, unlike the analytical method, it is unnecessary to estimate the type of damping that the system has. Finally, the proposed method is adapted to systems of different orders, managing to calculate second-order transfer functions equivalent to higher and lower orders. Response signals to the step of systems of an electrical, mechanical and electromechanical nature were used. In addition, tests were carried out with simulated signals and real signals to observe the behavior of the proposed method. In all cases, transfer functions were obtained to estimate the behavior of the system in a precise way before changes in the input. In all tests, it was shown that the use of the Final Value Theorem presents advantages compared to the use of algorithms without restrictions. Finally, it was revealed that the Gray Wolf Algorithm has a better performance for parametric estimation compared to the Jaya algorithm with an error up to 50% lower.

A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment.

Cancer is caused by the un-controlled division of abnormal cells in a body part. Various cancers exist in this world and one amongst them is breast cancer. Breast cancer (BC) threatens the lives of people and today, it is the secondary prime cause of death in women. Numerous research directions concentrated on the prediction of BC. The prevailing prediction model is time-consuming and have less accuracy. To trounce those drawbacks, this paper proposed a BC prediction system (BCPS) utilizing Optimized Artificial Neural Network (OANN). Primarily, the unprocessed BC data are regarded as the input. The big data (BD) storage comprises some repeated information. Secondarily, such repeated data are eliminated by utilizing Hadoop MapReduce. In the subsequent stage, the data are preprocessed utilizing replacing of missing attributes (RMA) and normalization techniques. Subsequently, the features are generally chosen by utilizing Modified Dragonfly algorithm (MDF). Then, the selected features are inputted for classification. Here, it classifies the features utilizing OANN. Optimization is done by employing the Gray Wolf Optimization (GWO) algorithm. Experiential outcomes are contrasted with prevailing IWDT (Improved Weighted-Decision Tree) in respect of precision, recall, accuracy, and ROC.

Time-Shift Multi-scale Weighted Permutation Entropy and GWO-SVM Based Fault Diagnosis Approach for Rolling Bearing.

Multi-scale permutation entropy (MPE) is an effective nonlinear dynamic approach for complexity measurement of time series and it has been widely applied to fault feature representation of rolling bearing. However, the coarse-grained time series in MPE becomes shorter and shorter with the increase of the scale factor, which causes an imprecise estimation of permutation entropy. In addition, the different amplitudes of the same patterns are not considered by the permutation entropy used in MPE. To solve these issues, the time-shift multi-scale weighted permutation entropy (TSMWPE) approach is proposed in this paper. The inadequate process of coarse-grained time series in MPE was optimized by using a time shift time series and the process of probability calculation that cannot fully consider the symbol mode is solved by introducing a weighting operation. The parameter selections of TSMWPE were studied by analyzing two different noise signals. The stability and robustness were also studied by comparing TSMWPE with TSMPE and MPE. Based on the advantages of TSMWPE, an intelligent fault diagnosis method for rolling bearing is proposed by combining it with gray wolf optimized support vector machine for fault classification. The proposed fault diagnostic method was applied to two cases of experimental data analysis of rolling bearing and the results show that it can diagnose the fault category and severity of rolling bearing accurately and the corresponding recognition rate is higher than the rate provided by the existing comparison methods.

Genomic Analysis Reveals Hypoxia Adaptation in the Tibetan Mastiff by Introgression of the Gray Wolf from the Tibetan Plateau.

The Tibetan Mastiff (TM), a native of the Tibetan Plateau, has quickly adapted to the extreme highland environment. Recently, the impact of positive selection on the TM genome was studied and potential hypoxia-adaptive genes were identified. However, the origin of the adaptive variants remains unknown. In this study, we investigated the signature of genetic introgression in the adaptation of TMs with dog and wolf genomic data from different altitudes in close geographic proximity. On a genome-wide scale, the TM was much more closely related to other dogs than wolves. However, using the 'ABBA/BABA' test, we identified genomic regions from the TM that possibly introgressed from Tibetan gray wolf. Several of the regions, including the EPAS1 and HBB loci, also showed the dominant signature of selective sweeps in the TM genome. We validated the introgression of the two loci by excluding the possibility of convergent evolution and ancestral polymorphisms and examined the haplotypes of all available canid genomes. The estimated time of introgression based on a non-coding region of the EPAS1 locus mostly overlapped with the Paleolithic era. Our results demonstrated that the introgression of hypoxia adaptive genes in wolves from the highland played an important role for dogs living in hypoxic environments, which indicated that domestic animals could acquire local adaptation quickly by secondary contact with their wild relatives.

Fear, foraging and olfaction: how mesopredators avoid costly interactions with apex predators.

Where direct killing is rare and niche overlap low, sympatric carnivores may appear to coexist without conflict. Interference interactions, harassment and injury from larger carnivores may still pose a risk to smaller mesopredators. Foraging theory suggests that animals should adjust their behaviour accordingly to optimise foraging efficiency and overall fitness, trading off harvest rate with costs to fitness. The foraging behaviour of red foxes, Vulpes vulpes, was studied with automated cameras and a repeated measures giving-up density (GUD) experiment where olfactory risk cues were manipulated. In Plitvice Lakes National Park, Croatia, red foxes increased GUDs by 34% and quitting harvest rates by 29% in response to wolf urine. In addition to leaving more food behind, foxes also responded to wolf urine by spending less time visiting food patches each day and altering their behaviour in order to compensate for the increased risk when foraging from patches. Thus, red foxes utilised olfaction to assess risk and experienced foraging costs due to the presence of a cue from gray wolves, Canis lupus. This study identifies behavioural mechanisms which may enable competing predators to coexist, and highlights the potential for additional ecosystem service pathways arising from the behaviour of large carnivores. Given the vulnerability of large carnivores to anthropogenic disturbance, a growing human population and intensifying resource consumption, it becomes increasingly important to understand ecological processes so that land can be managed appropriately.