Jackal Research Articles

Understanding the seasonal dietary patterns of the golden jackal (Canis aureus) for conservation implications in the Hirpora Wildlife Sanctuary, Western Himalayas

BackgroundWild canids form an important component of the ecosystem but are generally less studied and least prioritized for conservation. Diet is fundamental for effective management and exhibits the role of a species in an ecosystem. Golden jackal (Canis aureus, Linnaeus, 1758), a medium-sized canid species is known for opportunistic and omnivorous foraging behavior. MethodsThis research investigates the feeding ecology of golden jackals within the Hirpora Wildlife Sanctuary, Western Himalayas. From August 2020 to July 2022, trails (n = 9; 1.5–5.5 km) were walked seasonally (once per month) to collect fecal samples (n = 128) for investigation of seasonal diet composition and dietary niche breadth and biomass consumption of prey items by golden jackal. The association between the coefficient of digestibility value and food items was assessed using the Pearson correlation. We also conducted PCA to understand contribution of major dietary items. ResultsScat analysis revealed that jackals feed on both animal and plant matter. Animal matter constituted most of the diet, encompassing small mammals and domestic livestock, insects, and birds. Rodents emerge as the dominant dietary item, maintaining consistent prevalence year-round. Livestock carrion, notably domestic sheep formed a significant component across all seasons, with maximal consumption observed in summer. Notably absent from the jackals' diet were traces of threatened mountain ungulates. Domestic sheep emerged as the primary contributor (33.4 %) to the overall biomass consumed, highlighting the jackals' interaction with human-associated food resources. The research unveils an overall dietary niche breadth of 0.38, indicating a low degree of dietary diversity. Seasonal analysis indicates heightened dietary overlap in the autumn-summer transition, with rodents, domestic sheep, and birds being major contributors to this overlap. ConclusionThe findings underscore the adaptability of golden jackals to diverse food resources in a seasonal environment. This study provides foundational insights into the dietary ecology of jackal crucial for its conservation and management strategies in the ecologically fragile ecosystem. The finding gives a hint at the role jackals can play in regulating rodent populations and contributing to environmental cleanliness by scavenging.

Multi-Parameter Prediction of Solar Greenhouse Environment Based on Multi-Source Data Fusion and Deep Learning

In the process of agricultural production in solar greenhouses, the key to the healthy growth of greenhouse crops lies in accurately predicting environmental conditions. However, there are complex couplings and nonlinear relationships among greenhouse environmental parameters. This study independently developed a greenhouse environmental acquisition system to achieve a comprehensive method for the monitoring of the greenhouse environment. Additionally, it proposed a multi-parameter and multi-node environmental prediction model for solar greenhouses based on the Golden Jackal Optimization-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Self-Attention Mechanism (GCBS). The GCBS model successfully captures the complex nonlinear relationships in the greenhouse environment and accurately predicts changes in carbon dioxide concentration, air temperature and humidity, and soil temperature at different location nodes. To validate the performance of this model, we employed multiple evaluation metrics and conducted a comparative analysis with four baseline models. The results indicate that, while the GCBS model exhibits slightly higher computational time compared to the traditional Long Short-Term Memory (LSTM) network for time series prediction, it significantly outperforms the LSTM in terms of prediction accuracy for four key parameters, achieving improvements of 76.89%, 69.37%, 59.83%, and 56.72%, respectively, as measured by the Mean Absolute Error (MAE) metric.

Double trouble: Co-infection of Angiostrongylus vasorum and Dirofilaria immitis in golden jackal (Canis aureus) in Friuli Venezia Giulia, Italy

This study reports the first co-infection of Angiostrongylus vasorum and Dirofilaria immitis, two nematodes affecting canid cardiopulmonary systems, in golden jackals (Canis aureus) in Italy. Data on golden jackal carcasses, recovered in Friuli Venezia Giulia (Italy) from 2020 to 2023, were recorded using InfoFaunaFVG wildlife monitoring network. Out of 60 necropsied golden jackals, 24 tested positive for either pathogen, with 3 animals displaying co-infection. Pathological findings included verminous pneumonia, abdominal and thoracic bleeding, and adult individuals in the heart and pulmonary arteries. The recent expansion of the golden jackal in northern Italy may favour the establishment and spread of these nematodes, posing challenges for disease containment and surveillance. Given the increasing prevalence of angiostrongylosis and cardiopulmonary dirofilariosis in Europe, health monitoring of wild canids is essential to reduce their potential impact.

Flood assessment using machine learning and its implications for coastal spatial planning in Phu Yen Province, Vietnam

ABSTRACT The objective of this study was the development of a new machine learning model using a radial basis function neural network (RBFNN) to build flood susceptibility maps and damage assessment for the Phu Yen province of Vietnam. The built model will be optimized by five algorithms, namely Giant Trevally Optimization (GTO), Golden Jackal Optimization (GJO), Brown-Bear Optimization (BBO), Gray Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) to find out the best model to establish the flood susceptibility map. These models were evaluated using the statistical indices such as root mean square error (RMSE), mean absolute error (MAE), receiver operating characteristic (ROC), area under the curve (AUC), and coefficient of determination (COD). The result showed that all five optimization algorithms were successfully improving the performance of the RBFNN model, among them the hybrid model RBFNN–BBO has the highest performance with AUC = 0.998 and R2 = 0.8 and the RBFNN–GTO model has the lowest performance with AUC = 0.755 and R2 = 0.65. The regions identified with a high- and very-high flood susceptibility area (1,075 km2) were concentrated on the plain and along three of the largest rivers in Phu Yen province.

High-resolution computed tomographic (HRCT) image series from 413 canid and 18 felid skulls

Computed tomography (CT) is a non-invasive, three-dimensional imaging tool used in medical imaging, forensic science, industry and engineering, anthropology, and archaeology. The current study used high-resolution medical CT scanning of 431 animal skulls, including 399 dog skulls from 152 breeds, 14 cat skulls from 9 breeds, 14 skulls from 8 wild canid species (gray wolf, golden jackal, coyote, maned wolf, bush dog, red fox, Fennec fox, bat-eared fox), and 4 skulls from 4 wild felid species (wildcat, leopard, serval, caracal). This comprehensive and unique collection of CT image series of skulls can provide a solid foundation not only for comparative anatomical and evolutionary studies but also for the advancement of veterinary education, virtual surgery planning, and the facilitation of training in sophisticated machine learning methodologies.

Transformer partial discharge fault diagnosis based on improved adaptive local iterative filtering‐bidirectional long short‐term memory

AbstractInsulation deterioration, which is mainly caused by partial discharge (PD) occurring inside power transformers, is one of the prime reasons to cause transformer faults. Therefore, an effective diagnosis of PD is crucial to ensure the safe and stable operation of transformers. To extract more effective features that characterise transformers PD signals and enhance the recognition accuracy, a novel transformer PD fault diagnosis model based on improved adaptive local iterative filtering (ALIF) and bidirectional long short‐term memory (BILSTM) neural network is proposed. Addressing the issue of predetermined decomposition levels and accuracy in ALIF decomposition, the golden jackal optimisation (GJO) algorithm is introduced to optimise the parameters. The proposed fault diagnostic model extracts dominant PD features employing the improved ALIF and Refined Composite Multi‐Scale Dispersion Entropy and improves the diagnostic accuracy with the optimised BILSTM by introducing GJO. Experimental data evaluates the performance of support vector machine, long short‐term memory and BILSTM. The results verify the effectiveness and superiority of the proposed model.

From invaders to residents: The golden jackal (Canis aureus) expansion in Hungary since the mid-1990s.

The golden jackal has rapidly expanded across Europe in recent decades and was one of the first to reappear in Hungary. Using hunting bag data from the National Game Management Database from 1995 to 2021, we examined the spatial expansion of the species and its population dynamics. Our findings reveal an exponential increase in the golden jackal population since the mid-1990s, with an average annual growth rate of 40% in the hunting bag and an occupancy of 86% of the country. The average speed of the range expansion was 536.9 km2 year-1 until 2007 and increased to an average of 5,289.1 km2 year-1 during the second part of the expansion process. The density of shot golden jackals also shows an increasing trend with a substantial presence of outliers, indicating that nearly 10% of the hunting bag demonstrates exceptionally high numbers, particularly in southwestern Hungary. The spread originated in the southwest and extended towards the northeast and northwest. However, the increase of the hunting bag slowed down and the expansion rate has decreased in the previous two years, leading us to anticipate that the golden jackal population will stabilize soon, as the expansion has reached its maximum in Hungary. This indicates that from the initial settlement to the stabilization of the population, the time span can be two to three decades.

Sexual dimorphism in the Hungarian golden jackal population: analysing body and skull size and shape

AbstractDefining species within the Canidae family is challenging due to morphological convergence, behavioral plasticity, traditional taxonomic limitations, and possible hybridisation. This challenge is timely with the recent population and range expansion of the golden jackal (Canis aureus). Exploring their morphological data and sexual dimorphism is essential for identifying factors driving their success in new habitats. The proven hybridization of golden jackals with dogs and wolves may affect species description, population dynamics, and genetic diversity, impacting conservation strategies. This study, for the first time, conducts a morphometric analysis of golden jackals in Somogy County, Hungary, to prove sexual size dimorphism (SSD) in body and skull and sexual shape dimorphism (SShD) in skull across juvenile and adult age groups. 719 golden jackals (362 females and 357 males) were collected between January 2021 and January 2023. Descriptive statistics revealed significant SSD in body and skull measurements among both age groups, with males generally larger than females, particularly in body mass (11.72% in juveniles and 13.37% in adults). Most skull dimensions differed significantly between sexes and age groups, except for foramen magnum height, foramen magnum width, and postorbital breadth among juveniles and foramen magnum height and postorbital breadth among adults. We used principal component analyses (PCA) on raw dimension data and the log shape ratio method to extract shape information. Linear discriminant analysis (LDA) explored skull SShD between sexes. Notably, our study achieved over 71% accuracy in sex classification, illustrating the clear presence of SShD of the skull in golden jackals across both age groups. Our study provides a comprehensive database of golden jackals in the overpopulated Hungarian habitat, which will be helpful for further research on ecology, behavior, and conservation management.

Range expansion in the Golden Jackal, Canis aureus Linnaeus, 1758 (Canidae: Carnivora), into the Sinai Peninsula, Egypt

The Golden Jackal, Canis aureus Linnaeus, 1758 is widely distributed in Europe, Asia and the Middle East. Its range has considerably expanded in Europe over the past few decades. In the Middle East, the species occurs in Levant, including Israel and Palestine, just across the eastern border of Egypt. The first documented occurrence of the species in Egypt was in 2009 from a small area in extreme northeastern Sinai Peninsula, suggesting a very limited distribution within that part of the country. In this paper I report a recent range expansion of the Golden Jackal into large areas in eastern and southern Sinai Peninsula, including well-studied and monitored National Parks where this jackal has never been previously recorded, but is now quite common. I describe the new habitats now colonized by this jackal and discuss its ecological relations.

Avoiding visitors to a protected area increases predation risk for the endangered mountain gazelle

Abstract People's recreational activities in nature are essential to promote wildlife conservation. The interplay between humans and the environment is complex, as the presence of humans may impact wildlife both directly and by altering species interactions. Thus, conservation management in protected areas where people frequently interact with wildlife is challenging. We investigated the combined impact of different recreational activities of varying intensity, along with environmental and temporal factors, on the activity of the endangered mountain gazelle (Gazella gazella) and its predator, the golden Jackal (Canis aureus) in Ramat Hanadiv Nature Park—a protected area in northern Israel. We placed 109 camera traps in gazelles' activity centres over 5 years. We analysed the activity patterns of both species and how these patterns were affected by visitor activity. We found that gazelles became more nocturnal in areas with higher visitor activity, which led to greater overlap in their activity periods with golden Jackals. Both species were less active when there were more visitors in the park, but jackals were more attracted to disturbed areas, while gazelles avoided them. Furthermore, we found that gazelles' activity was primarily affected by visitor activity compared to environmental factors, study year or predator activity. Policy implications. We show direct and indirect effects of visitors on a predator–prey system and highlight the importance of understanding these impacts to manage protected areas and support human–wildlife coexistence. Read the free Plain Language Summary for this article on the Journal blog.

Characterization of Laser Drilling and Parametric Optimization Using Golden Jackal Optimizer

Characterization of Laser Drilling and Parametric Optimization Using Golden Jackal Optimizer

Molecular Detection and Genetic Variability of Hepatozoon canis in Golden Jackals (Canis aureus L. 1758) in Serbia.

Hepatozoon canis is a protozoan tick-borne parasite infecting domestic and wild canids, including foxes, wolves, and jackals. It is mainly found in dogs but has also been detected in several wild carnivores, including foxes, wolves, and jackals. Host transmission primarily occurs through the ingestion of infected ticks, typically Rhipicephalus sanguineus, with documented instances of transplacental transmission from infected females to cubs. In Serbia, the golden jackal is common throughout the country, and its population has increased in recent years. Previous research has documented the presence of several vector-borne pathogens in the jackal population in Serbia, so we conducted this study to determine the presence, prevalence, and genetic variability of H. canis. Over eleven years (2010-2020), 114 animal samples were collected from 23 localities in Serbia. A total of 90/114 (78.95%) jackals were positive for H. canis, and they came from 22 localities. Among 15 juveniles, almost half (6/15 (40%)) tested positive for H. canis. In addition to the high prevalence, high genetic variability of the pathogen was also found. According to the mutated positions, four sequence types (S4-S7) of H. canis were determined. Based on our earlier research on the grey wolf and on this study, it can be observed that various sequence types of H. canis circulate within wild canid populations in Serbia. The prevalence of H. canis infection in wild carnivores raises significant concerns for wildlife conservation and animal health. Infected animals may act as reservoirs for the disease, posing a potential risk to domestic animals by acting as a source of infection.

Genetic Identification of Putative Hybrids between Grey Wolf and Golden Jackal

Genetic Identification of Putative Hybrids between Grey Wolf and Golden Jackal

An improved golden jackal optimization algorithm for combined economic emission dispatch problems

In this research paper, a new improved golden jackal optimization (IGJO) algorithm is applied to address the combined economic emission dispatch (CEED) problem, along with various thermal generator constraints such as valve point loading (VPL) effect, generator limits (GL) in power system. The hunting behavior of the golden jackals is mimicked in the golden jackal optimization (GJO) algorithm. The main aim of the CEED problem is to find the best optimal generation scheduling while minimizing both fuel cost and emission besides meeting the different power system constraints. The original GJO algorithm faces challenges when dealing with high-dimensional optimization problems, as it tends to get trapped in local optima. To address this issue the opposition-based learning (OBL) method was adopted in this GJO algorithm to obtain the global optimal solution and ensure enhanced performance in finding the solution for the CEED problems. To assess the competitiveness of the IGJO algorithm, it is used for various CEED test problems available in the literature, and results are contrasted with other recent heuristic optimization algorithms. Simulation results show that the proposed IGJO performs more effectively than the other compared algorithms in terms of solution quality, and robustness.

The First Record of Golden Jackal Canis aureus in Pskov Oblast—Causes and Consequences

The First Record of Golden Jackal Canis aureus in Pskov Oblast—Causes and Consequences

An Improved Golden Jackal Optimization Algorithm Based on Mixed Strategies

In an effort to overcome the problems with typical optimization algorithms’ slow convergence and tendency to settle on a local optimal solution, an improved golden jackal optimization technique is proposed. Initially, the development mechanism is enhanced to update the prey’s location, addressing the limitation of just relying on local search in the later stages of the algorithm. This ensures a more balanced approach to both algorithmic development and exploration. Furthermore, incorporating the instinct of evading natural predators enhances both the effectiveness and precision of the optimization process. Then, cross-mutation enhances population variety and facilitates escaping from local optima. Finally, the crossbar strategy is implemented to change both the individual and global optimal solutions of the population. This technique aims to decrease blind spots, enhance population variety, improve solution accuracy, and accelerate convergence speed. A total of 20 benchmark functions are employed for the purpose of comparing different techniques. The enhanced algorithm’s performance is evaluated using the CEC2017 test function, and the results are assessed using the rank-sum test. Ultimately, three conventional practical engineering simulation experiments are conducted to evaluate the suitability of IWKGJO for engineering issues. The results obtained demonstrate the beneficial effects of the altered methodology and illustrate that the expanded golden jackal optimization algorithm has superior convergence accuracy and a faster convergence rate.

Evidence of southwestern distribution of the golden jackal in Prince Mohammed bin Salman Royal Reserve, Saudi Arabia

Abstract. Eid E, Smithson J. 2024. Evidence of southwestern distribution of the golden jackal in Prince Mohammed bin Salman Royal Reserve, Saudi Arabia. Biodiversitas 25: 1676-1681. The golden jackal, Canis aureus Linnaeus 1758, is listed as a species of globally Least Concern status by the IUCN; however, throughout the Arabian Peninsula, it is classified as Near Threatened due to the lack of information on its population and threats. Despite beings a widespread species, the information on the golden jackal distribution in Saudi Arabia is derived from limited historical records that have restricted its presence to the Hofuf area, around Al Asfah and Al Jawf in the Northeast, and very recent records in the agricultural areas in Tabuk governorate. In addition, photographic evidence of a juvenile individual was recorded in 2017 around the Jubail area. Here, we report the first confirmed Western distribution extension for Saudi Arabia, located in Prince Mohammed bin Salman Royal Reserve, and the most southwestern distribution worldwide. The jackal was recorded twice, suggesting a passing individual or a resident population, requiring further investigation and research. The presence of suitable ecosystems, habitats, and prey composition supports this. This record is important for Saudi Arabia and the Reserve as it guides the conservation efforts and highlights the importance of systematic surveys to understand the existing population, state, and threats.

Efficient malicious node detection by multi-objective energy trust aware hybrid optimization based maximizing lifetime of wireless sensor networks

Wireless sensor network (WSN) collect and detect data in real time, but their battery life limits their lifetime. The CH selection process increases network overhead and reduces lifetime, but it considers node processing and energy limitations. To solve that problem this research methodology proposed Multi Objective Energy trust - Aware Optimal Clustering and Secure Routing (MOETAOCSR) protocol. At first, the trust factors such as direct and indirect factors are calculated. Thus, the calculated values are given as input to the SDLSTM to detect the malicious node and normal node. Here, the network deployment process is initially carried out and then the cluster is formed by HWF-FCM. From the clustered sensor nodes, the cluster head is selected using Golden Jackal Siberian Tiger Optimization (GJSTO) approach. Then, the selection of CH the paths are learned by using the Beta Distribution and Scaled Activation Function based Deep Elman Neural Network (BDSAF-DENN) and from the detected paths the optimal paths are selected using the White Shark Optimization (WSO). From the derived path sensed data securely transferred to the BS for further monitoring process using FPCCRSA. The proposed technique is implemented in a MATLAB platform, where its efficiency is assessed using key performance metrics including network lifetime, packet delivery ratio, and delay. Compared to existing models such as EAOCSR, RSA, and Homographic methods, the proposed technique achieves superior results. Specifically, it demonstrates a 0.95 improvement in throughput, 0.8 enhancement in encryption time, and a network lifetime of 7.4.

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm’s ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch

An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch