Golden Eagle Research Articles

Bill shape variation in selected species in birds of prey.

At the top of many ecosystems, raptors, also known as birds of prey, hold major influence. They shape their surroundings through their powerful hunting skills and complex interactions with their environment. This study investigates the beak morphology of four prominent raptor species, Golden eagle (Aquila chrysaetos), Common buzzard (Buteo buteo), Peregrine falcon (Falco peregrinus) and Common kestrel (Falco tinnunculus), found in Türkiye. By employing geometric morphometric methods, we investigate shape variations in the beaks of these species to unravel the adaptive significance of their cranial structures. This analysis reveals distinct beak morphologies among the studied raptors, reflecting adaptations to their feeding habits, hunting techniques and ecological niches. The results from Principal component analysis and Canonical variate analysis demonstrate significant differences in beak morphology between the Falconiformes and Accipitriformes clades, as well as among all three groups. The overall mean beak shapes of Golden Eagles are quite similar to Common Buzzards, with both species having longer beaks. In contrast, Falcons exhibit a distinctly different beak morphology, characterized by wider and shorter beaks. Changes in beak shape can lead to changes depending on the skull. It is thought that skull shape variations among predator families may have an impact on beak shape. These findings highlight the importance of integrating morphometric analyses with ecological insights to enhance our understanding of the evolutionary processes shaping raptor beak morphology.

Heterospecific visual cues and trophic facilitation processes used by a solitary bone‐eating vulture

AbstractWhile the influence of public information sharing on foraging strategies is of growing interest, empirical studies exploring intraguild social information use and facilitation roles between individuals with different trophic specializations remain scarce. Heterospecific facilitation should be more common in specialist foragers, for example, the bearded vulture Gypaetus barbatus, a solitary bone‐eating scavenger. We monitored 133 carcasses of different types/sizes in open and shrubland landscapes in the Spanish Pyrenees to explore bearded vulture foraging in relation to social information transfer and adaptive trophic behavior. We hypothesized that they might (1) feed on carcasses after initial heterospecific exploitation; (2) use heterospecifics to locate and/or exploit carcasses; and (3) prefer old carcasses over fresh ones. We recorded bearded vultures scavenging at 44 carcasses; 95.5% had been previously exploited by heterospecifics (93.2% by griffon vultures Gyps fulvus and 2.3% by golden eagles Aquila chrysaetos) while only two small‐sized carcasses were scavenged without previous heterospecific exploitation. Bearded vultures were not observed scavenging at carcasses used only by mammals. Both griffon and bearded vultures took longer to find carcasses in shrublands than in open landscapes. However, bearded vulture arrival times between landscapes after carcass discovery by griffon vultures were similar. Bearded vultures were more likely to discover a carcass the greater the number of griffon vultures exploiting it. Only 10.4% bearded vulture scavenging events occurred after the third week following heterospecific exploitation, suggesting that recently opened carcasses were preferred. Clearly, heterospecifics play an essential role in bearded vultures foraging success, and griffon vultures are fundamental facilitators for specialist foragers, both in providing visual cues to food location and in opening up carcasses to enable access to food.

Fault causes and its detection in standalone PV system using ANN and GEO technique

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.

Фауна и население птиц гористых районов южного берега Варангер-фьорда

The article presents data from field studies of the fauna and bird population of mountainous areas of the Varangerfjord southern coast. This area has erspectives to be included in the boundaries of the projected “Voryema” Federal Reserve. We carried field studies during the 2019–2021 breeding period in the northwestern part of the Pechengsky district of the Murmansk region. In the course of the work, we surveyed mountainous areas from areas adjacent to the Voryema River valley to areas near the iinahamari settlement. The total length of survey routes was 81.8 km. Analysis of the received field materials shows that at least 53 bird species occur in the breeding season. Tundra and rocky areas are breeding sites for skuas, gulls, loons, and a number of sandpipers’ and ducks’ species. The combination of different habitats creates the possibility of the presence of four species of pipits on this territory at once. Therefore, this territory can be a good model for studying the effects of climate change on a number of closely related species living together. In general, the results of the study show that the surveyed territories of the mountainous areas of the Varangerfjord southern coast are promising for inclusion in the boundaries of the projected Voryema Federal Reserve. They have a characteristic bird population structure and serve as breeding grounds for a number of rare species (whooper swan, whooper, golden eagle, white-tailed eagle, kestrel, white-throated thrush).

Fauna and Landscapes of the Southern Minusinsk Basin (Southern Siberia) at the End of the Late Pleistocene.

Paleolandscapes of the first half of MIS 2 or the Last Glacial Maximum (LGM) were reconstructed based on the study of the first mammoth fauna locality found in the Abakan River valley and a comparison with other localities of the South Minusinsk Basin. Sediments of the Uytag geological section under study included silt interlayers underlain and overlain by sandstone rock slack fragments and were interpreted as weakly eroded diluvium. Studies of the Uytag locality made it possible to clarify the time and range of distribution in southern Siberia for several mammals (Ovis ammon, Equus ferus, Marmota baibacina, Sicista subtilis, Lagurus lagurus, and Microtus gregalis) and the Pleistocene bird Aquila chrysaetos. The age of the locality was confirmed by radiocarbon dating. The species composition of the Uytag fauna was similar to that of other localities of the same age and region. All known localities and single finds of faunal remains indicate that open steppe landscapes were widely developed in the South Minusinsk Basin during the LGM.

PREDATION OF TURKEY VULTURES BY GOLDEN EAGLES IN WALNUT CANYON NATIONAL MONUMENT, ARIZONA

Golden Eagles (Aquila chrysaetos) and their diets have been studied extensively in North America and beyond. There have, however, been minimal studies of this species conducted in the state of Arizona, and none within Walnut Canyon National Monument. This ongoing study is focused on the diet of Golden Eagles that have nested in Walnut Canyon National Monument since at least 2008. Analysis of prey remains collected from their nests provides a baseline sample of their dietary habits, but also reveals a much higher proportion of Turkey Vultures (Cathartes aura) than is reported from any other Golden Eagle study in the country, with vultures making up more than 24% of individuals identified from the assemblage.

Wildlife following people: A multidisciplinary assessment of the ancient colonization of the Mediterranean Basin by a long‐lived raptor

Abstract Modern humans widely shaped present ecosystems through intentional and unintentional geographical redistribution of wildlife, both in historical and pre‐historical times. However, the patterns of ancient human‐mediated indirect changes in wildlife range are largely unknown, and the mechanisms behind them remain obscure. We used a multidisciplinary approach to (a) reconstruct the process of colonization of the Mediterranean Basin by a long‐lived bird of prey, the Bonelli's eagle (Aquila fasciata), and (b) test the hypothesis that this colonization was unintentionally favoured by anatomically modern humans through a release of competition by dominant species, primarily golden eagles (A. chrysaetos). The fossil record of Bonelli's eagles in the Mediterranean Basin was restricted to the last c. 50 ky. This timing matches the period of modern human presence in Europe. Distribution modelling showed that Bonelli's eagles find more suitable conditions in interglacial periods, while glacial maxima are largely unfavourable unless in coastal refugia. In agreement with this, all Bonelli's eagle's fossils were found in coastal areas, and demographic inference from genetic data revealed a drop in the effective population size by around the last glacial maximum. In today's communities, we found a strongly asymmetric competitive relationship between (subordinate) Bonelli's and (dominant) golden eagles, with the former occupying far more humanized areas than the latter both at the landscape scale and the local (i.e. nesting cliff) scale. Moreover, the nesting habitat overlap analysis indicated that, in the absence of the other species, a notably higher population of Bonelli's eagle, but not of golden eagle, could be expected. Our findings are consistent with the human‐mediated competitor release hypothesis, by which anatomically modern humans could have unintentionally favoured the large‐scale colonization by Bonelli's eagles of a previously competitively hostile Mediterranean Basin. Reconstructing the role of ancient humans in shaping present ecosystems may help to understand the historical, current and future population trajectories of competing species of conservation concern under the ongoing scenario of global environmental change. It also illustrates how human‐mediated apparent competition may promote large‐scale redistribution and colonization of wildlife, including long‐lived species. Read the free Plain Language Summary for this article on the Journal blog.

Automatic calibration of a discrete element model of a masonry arch by swarm intelligence methods

This paper addresses the automatic calibration of constitutive parameters of the Discrete Element Method (DEM) models of masonry structures. Three swarm intelligence algorithms were applied, which are efficient in problems characterised by the small number of unknown parameters but high computational demand for the evaluation of the target function. Trust-Based Particle Swarm Optimisation (TBPSO) was chosen due to its excellent convergence rate. It was already tested that TBPSO converged faster than the original Genetic Algorithm and Particle Swarm Optimisation Methods. Therefore, TBPSO was compared with two recent and promising methods, the Marine Predator Algorithm (MPA) and the Golden Eagle Optimiser (GEO). The paper also presents a novel two-step approach, which enhances the performance of the tested optimisation strategies. The methodology is capable to account for and precisely find the mode of failure using Convolutional Neural Networks (CNN). The three optimisation methods and the two-step approach were applied on the DEM model of a quasi-static masonry arch collapse experiment from the literature. Performance of the three algorithms in identifying the appropriate constitutive parameters values of the DEM model were compared. Clear evidence on the advantage of the two-step approach with TBPSO is demonstrated. Remarks about different modelling issues are also included.

Cooperative Breeding by Golden Eagles (Aquila chrysaetos) in Colorado

Cooperative Breeding by Golden Eagles (Aquila chrysaetos) in Colorado

Radio Resource Scheduling in 5G Networks Based on Adaptive Golden Eagle Optimization Enabled Deep Q-Net

Radio Resource Scheduling in 5G Networks Based on Adaptive Golden Eagle Optimization Enabled Deep Q-Net

Heating load prediction in buildings using decision tree machine learning method

In recent years, the burgeoning imperative of energy-efficient building management practices has surged dramatically, underscoring an urgent mandate for comprehensive studies that integrate cutting-edge optimization algorithms with precise heating load forecasting techniques. These studies are not merely endeavors; they represent concerted efforts to increase building energy efficiency and address mounting concerns regarding sustainability and resource utilization. In the intricate domain of heating, ventilation, and air conditioning (HVAC) systems, energy optimization challenges are being meticulously confronted through rigorous exploration and the application of innovative problem-solving methodologies. This pioneering study introduces groundbreaking methodologies by seamlessly integrating two state-of-the-art optimization algorithms— the Red Fox Optimization and the Golden Eagle Optimizer— with the Decision Tree model. This fusion is aimed at enhancing the accuracy of heating load predictions and streamlining HVAC system optimization processes, marking a significant leap toward achieving heightened energy efficiency and operational efficacy in building management practices. The study emphasizes the significance of precise heating load prediction in advancing energy efficiency, realizing cost savings, and fostering environmental sustainability in building management. Furthermore, it delves into the multifaceted impact of various building features on heating load, encompassing variables such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. These insights furnish actionable intelligence for refined decision-making processes in both building design and operation. Based on the results, the DT single model experienced the weakest performance among the three models, with R2 = 0.975 and RMSE = 1.608. The model DTFO (DT + FOX) achieves an extraordinary R2 value of 0.996 and RMSE value of 0.961 for heating load prediction, surpassing the performance benchmarks set by other models. This achievement holds considerable promise for aiding engineers in crafting energy-efficient buildings, particularly within the swiftly evolving landscape of smart home technologies.

Conquering the Labor Shortage With Kiosks

The labor shortage within the restaurant industry has caused service quality and revenue to decline. To help combat this decline, restaurant operators have begun adopting kiosks and limiting their menu options. This case study addresses labor and consumer issues that are being experienced within a quick service seafood restaurant called Golden Eagle Fish Shack. The owner is interested in implementing kiosks into his operation to help overcome the declining consumer satisfaction from the longer wait times, alleviate time consuming tasks from his current employees, while also to increase the revenue stream of the operation.

Observations of golden eagles attacking and consuming wolf pups

Although lethal interactions between wolves and other apex carnivores have been reported for a handful of species, the killing or consumption of wolves by free-ranging golden eagles has rarely been reported. Here, we present four opportunistic field observations documenting the consumption of a wolf pup by free-ranging golden eagles and predatory behavior of eagles towards wolf pups. Although we could not confirm the killing and consumption of wolves by eagles in a single predatory event, actual predation seems plausible based on our observations. We hypothesize that these intraguild apex predators’ interactions, consisting on a smaller raptor predator attacking or consuming a lager mammal and social predator, are rare events, but provide valuable information on potential relationships across keystone apex predators.

Key breeding habitats of threatened golden eagles across Eastern Canada identified using a multi-level, multi-scale habitat selection approach

ContextIn a conservation context, identifying key habitats suitable for reproduction, foraging, or survival is a useful tool, yet challenging for species with large geographic distributions and/or living in remote regions.ObjectivesThe objective of this study is to identify selected habitats at multiple levels and scales of the threatened eastern North American population of golden eagles (Aquila chrysaetos). We studied habitat selection at three levels: landscape (second order of selection), foraging (third order of selection), and nesting (fourth order of selection).MethodsUsing tracking data from 30 adults and 366 nest coordinates spanning over a 1.5 million km2 area in remote boreal and Arctic regions, we modelled the three levels of habitat selection with resource selection functions using seven environmental features (aerial, topographical, and land cover). We then calculated the relative probability of selection in the study area to identify regions with higher probabilities of selection.ResultsEagles selected more for terrain ruggedness index and relative elevation than land cover (i.e., forest cover, distance to water; mean difference in relative selection strength: 1.2 [0.71; 1.69], 95% CI) at all three levels. We also found that the relative probability of selection at all three levels was ~ 25% higher in the Arctic than in the boreal regions. Eagles breeding in the Arctic travelled shorter foraging distances with greater access to habitat with a high probability of selection than boreal eagles.ConclusionHere we found which aerial and topographical features were important for several of the eagles’ life cycle needs. We also identified important areas to monitor and preserve this threatened population. The next step is to quantify the quality of habitat by linking our multi-level, multi-scale approach to population demography and performance such as reproductive success.

Using juvenile movements as a proxy for adult habitat and space use in long‐lived territorial species: a case study on the golden eagle

Effective conservation management of wildlife species depends on understanding their space and habitat use. Telemetry has become the primary source of data for information on how species use space and habitats. However, animals can be difficult to capture, leading to limited sample sizes and thus low quality inferences. As some individuals may be easier to capture than others, it may be tempting to use them to make inferences about the studied population as a whole. Juvenile birds, in contrast to adults, are easy to capture while they are still in the nest. However, there are few studies on when and how they might serve to obtain a representative characterization of the habitat or space use of adults. This study investigated this by using GPS‐tracking data of 35 adult/juvenile dyads of golden eagles Aquila chrysaetos, with the juvenile and adult in a dyad sharing the same home‐range. We assessed juvenile‐to‐adult home‐range overlap and also compared their relative use of habitats within that space. We also analysed how these metrics evolved throughout the post‐fledging dependence period (PFDP). During this period, juvenile‐to‐adult similarity was more than 80% for the entire home‐range, whereas it was lower for the core area (approximately 60%). Habitat‐use similarity was high, at approximately 90% for both the home‐range and core area, both in land‐cover and topography. The similarity increased following the improvement of juvenile flight skills over a period of two months, to the extent that two months after fledging and until the end of the PFDP, habitat and space use of juveniles can be used to infer the home‐range and habitat requirements of adults. It would be valuable to study this ‘adult‐by‐juvenile replacement' approach in other species to determine whether it could be generalized, notably for species with a shorter dependence period or more complex social interactions.

Loading pattern optimization of VVER-1000 reactor core based on the discrete golden eagle optimization algorithm

The main features of the loading pattern optimization (LPO) problem, such as high-dimensionality, multi-modality, and non-linearity, make it difficult to achieve a truly optimal configuration. In recent years, metaheuristic methods have been successfully used to solve this problem. In this research, a discrete golden eagle optimization (DGEO) algorithm has been developed to solve the LPO problem in the first cycle of VVER-1000 reactor core. To evaluate the proposed algorithm, a linear multi-purpose fitness function has been used to improve the safety parameters of the reactor core by obtaining a flatter power distribution during the first cycle, and also to enhance the economic parameters by increasing the cycle length and reducing the cost of fuel recycling. For this purpose, a FORTRAN program has been written to map the DGEO algorithm for the LPO problem using the PMAX and PARCS core calculation code to compute the fitness function in each iteration. To speed up the calculations, parallel computing has been applied in the written program. The results demonstrated that the loading pattern, which is suggested by the DGEO algorithm, enhances all the safety and economic parameters in the fitness function. Thus, the DGEO algorithm is highly reliable for the LPO problems in the VVER 1000 reactor core.

Protecting breeding sites: a critical goal for the conservation of the golden eagle in Mexico under global change scenarios

Impacts of global climate and land‐use changes on distribution patterns and breeding sites remain today poorly studied for several vulnerable emblematic bird species, including the Golden Eagle (Aquila chrysaetos). Herein, we analyzed the potential effect of global climate changes and agricultural activities on the distribution patterns of this top predator across Mexico. We assessed the long-term role of protected areas (PAs) for safeguarding the species’ overall distribution and its breeding sites. We evaluated current and future (2040s, 2060s, and 2080s) threats from global change using ecological niche modeling and geographic information system approaches to determine the percentage of the species’ distribution area that overlaps with highly human-modified areas and PAs under each climate scenario. We also used niche overlap tests to assess whether the species’ breeding sites show equivalence or similarity of climatic conditions over time. Our findings revealed shifts in the Golden Eagle’s distributional area, with an overall size reduction (by ~ 57% in the 2040s and ~ 78% in the 2080s) due to future environmental changes, mainly attributable to increasingly dry and warm conditions. Mexican PAs cover ~ 12% of the Golden Eagle’s range across country, but this decreased by > 33% on average under the species’ future distributions. Although the hypothesis of equivalent climatic conditions at breeding sites over time was rejected, those sites did have long-term climate similarity (niche overlap: 0.75–0.83; P < 0.05). Considering the species’ nest site fidelity and that colonization of new areas within Mexico seems unlikely, protection of these breeding sites is a critical step for the long-term conservation of this emblematic species in Mexico.

Golden Eagle Populations, Movements, and Landscape Barriers: Insights from Scotland

GPS satellite tracking allows novel investigations of how golden eagles Aquila chrysaetos use the landscape at several scales and at different life history stages, including research on geographical barriers which may prevent or limit range expansion or create population/sub-population isolation. If there are significant barriers to golden eagle movements, there could be demographic and genetic consequences. Genetic studies have led investigations on the identification of sub-species, populations, and sub-populations but should be conjoined with demographic studies and dispersal movements to understand fully such designations and their geographic delimitation. Scottish eagles are genetically differentiated from continental European birds, with thousands of years of separation creating a distinct population, though without sub-species assignation. They present unique research opportunities to examine barriers to movements illustrated by satellite tracking under Scotland’s highly variable geography. We primarily examined two features, using more than seven million dispersal records from satellite tags fitted to 152 nestlings. The first was the presence of unsuitable terrestrial habitat. We found few movements across a region of largely unsuitable lowland habitat between upland regions substantially generated by geological features over 70 km apart (Highland Boundary Fault and Southern Uplands Fault). This was expected from the Golden Eagle Topography model, and presumed isolation was the premise for an ongoing reinforcement project in the south of Scotland, translocating eagles from the north (South Scotland Golden Eagle Project: SSGEP). Second was that larger expanses of water can be a barrier. We found that, for a northwestern archipelago (Outer Hebrides), isolated by ≥24 km of sea (and with prior assignation of genetical and historical separation), there were no tagged bird movements with the Inner Hebrides and/or the Highlands mainland (the main sub-population), confirming their characterisation as a second sub-population. Results on the willingness of eagles to cross open sea or sea lochs (fjords) elsewhere in Scotland were consistent on distance. While apparently weaker than the Outer Hebrides in terms of separation, the designation of a third sub-population in the south of Scotland seems appropriate. Our results validate the SSGEP, as we also observed no movement of birds across closer sea crossings from abundant Highland sources to the Southern Uplands. Based on telemetric results, we also identified where any re-colonisation of England, due to the SSGEP, is most likely to occur. We emphasise, nevertheless, that our study’s records during dispersal will be greater than the natal dispersal distances (NDDs), when birds settle to breed after dispersal, and NDDs are the better shorter arbiter for connectivity.

Equilibrium population dynamics of site-dependent species

Adults of site-dependent species require a discrete structure, e.g., a cavity, for breeding, which they are unable to construct and must locate and occupy. The environment provides only a limited number of such sites, which may vary in overall quality due to their environmental context. Heterogeneity of site quality can result in population equilibrium, often construed as source-sink dynamics. Rodenhouse et al. (Ecology 78:2025-2042, 1997) proposed a mechanism of site-dependent equilibrium that they claimed was more general than source-sink dynamics. After defining notions of source and sink, I use explicit dynamical models for a site-dependent population, based on the life history of golden eagles (Aquila chrysaetos), with two levels of site quality, to investigate the existence of population equilibria under several scenarios: source-source, source-sink, and source-floater. The life history traits I employ are not overly restrictive and serve the purpose only of providing models explicit enough to be treated analytically. I use a generalized notion of “golden eagle” since site dependency is often discussed in the literature on raptors, and I have exploited details from Hunt et al. (PLoS ONE 12:e0172232, 2017) for numerical simulations. The crucial features of the modeling, however, are those of site dependency. The modeling emphasizes that equilibrium results from the limited supply of source sites and that vital rates averaged across site qualities do not provide a compelling explanation of equilibria, contra Rodenhouse et al. Counterintuitively, equilibria are theoretically possible, even when both site qualities are intrinsically source sites.

Investigation of mechanical properties of high-performance concrete via optimized neural network approaches

In this paper, an artificial intelligence approach has been employed to analyze the slump and compressive strength (CS) of high-performance concrete (HPC), focusing on its mechanical properties. The importance of assessing these critical concrete characteristics has been widely acknowledged by experts in the field, leading to the development of innovative methods for estimating parameters that typically require laboratory testing. These intelligent techniques improve the accuracy of mechanical property predictions and reduce the resource-intensive and costly nature of experimental work. The radial basis function neural network (RBFNN) is the foundational model for predicting the mechanical attributes of various HPC mixtures. To fine-tune the RBFNN’s performance in replicating the mechanical properties of HPC samples, two optimization algorithms, namely the Golden Eagle Optimizer (GEO) and Dynamic Arithmetic Optimization Algorithm (DAOA), have been employed. In this manner, both RBGE and RBDA models were trained using a dataset comprising 181 HPC samples that included superplasticizers and fly ash. The results show that DAOA has significantly improved the base model’s predictive capability, achieving a higher correlation with a value R2 of 0.936 when estimating slump. Furthermore, RBDA exhibited a more favorable root mean square error (RMSE) in predicting compressive strength compared to RBGE, with a notable 16% difference. Ultimately, both integrated models demonstrated their effectiveness in accurately modeling the mechanical properties of HPC.