Anser Anser Research Articles

Exploring Shape Variance in Waterbirds' Pad Feet: A Geometric Morphometric Analysis

Waterbirds usually have webbed feet, which help them move easily through water. These pad feet fall into four main categories: palmate, semipalmate, totipalmate, and lobate. In this study, morphological diversity among the pad feet of different waterbirds breeds such as the West Indian whistling duck (Anas bahamensis), mandarin duck (Aix galericulata), red-breasted goose (Branta ruficollis), wood duck (Aix sponsa), mute swan (Cygnus olor), greylag goose (Anser anser), mallard (Anas platyrhynchos), Pekin duck (Anas platyrhynchos domesticus), redhead duck (Aythya americana), Egyptian goose (Alopochen aegyptiaca), and pelican (Pelecanus onocrotalus) was examined by the geometric morphometric method. 2D images of 12 waterbirds pad feet from different parts of Türkiye were analyzed from dorsal view. In total thirteen landmarks were used. The analysis focused on principal component 1 and principal component 2 values. Principal component 1 shows slightly greater changes occurring on the lateral toes II and IV, as well as in the interdigital webbing below the average. Principal component 2 reveals greater shape changes also on the toes II and IV, which are more lateral. Geometric morphometric analysis proves valuable in identifying variations in the shape of the pad feet among various breeds of waterbirds, making it an effective tool for taxonomic purposes.

Diversity and Population of Ducks and Geese in Keibul Lamjao National Park (KLNP) in Manipur, India

As a part of the Asian Waterbird Census (AWC), surveys on ducks and geese were made at five (5) major waterbird congregation sites of Keibul Lamjao National Park (KLNP) during 2020-2024. A total of 18 species of ducks and two species of geese belonging to the family of Anatidae were recorded during the period. The duck species includes 14 migratory and 4 resident species and the two migratory geese species. Lesser Whistling Duck Dendrocygna javanica was the most abundant and dominant species in terms of population followed by Gadwall Anas strepera and Red - crested Pochard Netta rufina and minimum value was observed in Greylag Goose Anser anser. The Critically Endangered Baer’s Pochard Aythya fuligula was recorded in the year 2023 at Pabot Chingmang site (2 individuals). Another two globally Near threatened (NT) Ferruginous Pochard Aythya nyroca and Falcated Duck Anas falcata have been recorded here.

Optimization of Feature Selection Using Greylag Goose Optimization Algorithm for Monkeypox

Optimization of Feature Selection Using Greylag Goose Optimization Algorithm for Monkeypox

Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification

Lung cancer is an important global health problem, and it is defined by abnormal growth of the cells in the tissues of the lung, mostly leading to significant morbidity and mortality. Its timely identification and correct staging are very important for proper therapy and prognosis. Different computational methods have been used to enhance the precision of lung cancer classification, among which optimization algorithms such as Greylag Goose Optimization (GGO) are employed. These algorithms have the purpose of improving the performance of machine learning models that are presented with a large amount of complex data, selecting the most important features. As per lung cancer classification, data preparation is one of the most important steps, which contains the operations of scaling, normalization, and handling gap factor to ensure reasonable and reliable input data. In this domain, the use of GGO includes refining feature selection, which mainly focuses on enhancing the classification accuracy compared to other binary format optimization algorithms, like bSC, bMVO, bPSO, bWOA, bGWO, and bFOA. The efficiency of the bGGO algorithm in choosing the optimal features for improved classification accuracy is an indicator of the possible application of this method in the field of lung cancer diagnosis. The GGO achieved the highest accuracy with MLP model performance at 98.4%. The feature selection and classification results were assessed using statistical analysis, which utilized the Wilcoxon signed-rank test and ANOVA. The results were also accompanied by a set of graphical illustrations that ensured the adequacy and efficiency of the adopted hybrid method (GGO + MLP).

Individual responses of GPS‐tagged geese scared off crops by drones or walking humans

Abstract Scaring of wildlife is commonly used in attempts to reduce crop damage in agricultural landscapes, but relatively few studies exist on its actual effect. We tracked GPS‐tagged greylag geese (Anser anser) in south‐central Sweden, before and after scaring by approaching them either by walking or by drone. On the field level, we studied the scaring effect by estimating return rate to the field where scared. On the landscape level, we tested if geese were less prone to use agricultural fields after being scared. Geese immediately left the field when scared and 5 min later they were on average 990 m (±56 SE) from the scaring position. The proportion of GPS positions near the scaring position decreased significantly for at least 4 h after scaring. Geese showed a significant shift from agricultural fields to wetland habitats the first 4 h after scaring. However, the effect of scaring soon levelled off; after 24 h the field where scaring had occurred was used to the same extent as any other field in the landscape, and agricultural fields were used to the same extent by scared and undisturbed geese. We did not find any differences in response depending on scaring technique. The probability to return and use agricultural fields after scaring was higher for geese scared in the morning compared to in the afternoon. Moreover, the probability to return and use of agricultural fields were higher in spring than in other seasons. Practical implication. We found that scared geese tend to visit agricultural fields soon after scaring and that scaring alone tends ‘to move the problem around’. This suggests that scaring needs to be repeated across the landscape, but also that accommodation fields where geese do not cause damage may be needed to keep geese off conventional fields. However, our study presents a glimpse of promise as the rather simple drone used covers large areas quickly and minimizes walking in growing crops. With technological advancement and possible autonomous techniques, drones may be capable of providing repeated scaring over large areas in the future.

An efficient water quality index forecasting and categorization using optimized Deep Capsule Crystal Edge Graph neural network.

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field. The innovation lies in the integration of a Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm for precise feature selection, ensuring that the most relevant indicators of water quality (WQ) are utilized. Furthermore, the use of the Greylag Goose Optimization Algorithm to fine-tune the neural network's weight parameters enhances the model's predictive accuracy. This dual optimization framework significantly improves WQI prediction, achieving a remarkable mean squared error (MSE) of 6.7 and an accuracy of 99%. By providing a robust and highly accurate method for WQ assessment, this research offers a powerful tool for environmental authorities to proactively manage river WQ, prevent pollution, and evaluate the success of restoration efforts. PRACTITIONER POINTS: Novel method combines optimization and Deep Capsule Crystal Edge Graph for WQI estimation. Preprocessing includes data cleanup and feature selection using advanced algorithms. Deep Capsule Crystal Edge Graph neural network predicts WQI with high accuracy. Greylag Goose Optimization fine-tunes network parameters for precise forecasts. Proposed method achieves low MSE of 6.7 and high accuracy of 99%.

Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences.

Computer Vision Application for Effective Flood Forecasting in Public Health Systems

Among the natural disasters, floods are considered as one of the most devastating since they can damage infrastructures, force people leave their homes, and negatively impact the public health. The training of the model seems to be computationally intensive and could prove an interruption where computational resources are a limited commodity. In this paper, we present the Greylag goose driven redefined Long Short-Term Memory (GG-RLSTM) model to be implemented by public health systems in effective flood forecasting. Thus, adapting behavioural patterns in Greylag geese, GG-RLSTM enhances the theoretical structure of LSTM and increases its capability to capture complex relations in the flood processes. From the Kaggle dataset, we gathered meteorological evidence, and geographical features. The results from the experiments indicate that effectiveness of the proposed GG-RLSTM model is higher than the other conventional methods in terms of accuracy (89%), precision (88%), recall (87%), and f1-score (90%). Due to the effectiveness of the model and its applicability in various situations, public health systems can likely adopt it and commence preventive flood measures.

Air pollution prediction using blind source separation with Greylag Goose Optimization algorithm

Particularly, environmental pollution, such as air pollution, is still a significant issue of concern all over the world and thus requires the identification of good models for prediction to enable management. Blind Source Separation (BSS), Copula functions, and Long Short-Term Memory (LSTM) network integrated with the Greylag Goose Optimization (GGO) algorithm have been adopted in this research work to improve air pollution forecasting. The proposed model involves preprocessed data from the urban air quality monitoring dataset containing complete environmental and pollutant data. The application of Noise Reduction and Isolation techniques involves the use of methods such as Blind Source Separation (BSS). Using copula functions affords an even better estimate of the dependence structure between the variables. Both the BSS and Copula parameters are then estimated using GGO, which notably enhances the performance of these parameters. Finally, the air pollution levels are forecasted using a time series employing LSTM networks optimized by GGO. The results reveal that GGO-LSTM optimization exhibits the lowest mean squared error (MSE) compared to other optimization methods of the proposed model. The results underscore that certain aspects, such as noise reduction, dependence modeling and optimization of parameters, provide much insight into air quality. Hence, this integrated framework enables a proper approach to monitoring the environment by offering planners and policymakers information to help in articulating efficient environment air quality management strategies.

Potato Leaf Disease Classification Using Optimized Machine Learning Models and Feature Selection Techniques

AbstractThe diseases that particularly affect potato leaves are early blight and the late blight, and they are dangerous as they reduce yield and quality of the potatoes. In this paper, different machine learning (ML) models for predicting these diseases are analysed based on a detailed database of more than 4000 records of weather conditions. Some of the critical factors that have been investigated to determine correlations with disease prevalence include temperature, humidity, wind speed, and atmospheric pressure. These types of data relationships were comprehensively identified through sophisticated means of analysis such as K-means clustering, PCA, and copula analysis. To achieve this, several machine learning models were used in the study: logistic regression, gradient boosting, multilayer perceptron (MLP), and support vector machine (SVM), as well as K-nearest neighbor (KNN) models both with and without feature selection. Feature selection methods such as the binary Greylag Goose Optimization (bGGO) were applied to improve the predictive performance of the models by identifying feature sets pertinent to the models. Results demonstrated that the MLP model, with feature selection, achieved an accuracy of 98.3%, underscoring the critical role of feature selection in improving model performance. These findings highlight the importance of optimized ML models in proactive agricultural disease management, aiming to minimize crop loss and promote sustainable farming practices.

Optimizing electric vehicle paths to charging stations using parallel greylag goose algorithm and Restricted Boltzmann Machines

Optimizing electric vehicle paths to charging stations using parallel greylag goose algorithm and Restricted Boltzmann Machines

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This study is to identify the comparative morphological description of the tongue in two species of domesticated waterbirds, Mallard Anas platyrhynchos (Linnaeus,1758) and Graylag Goose Anser anser (Linnaeus,1758) (Anseriformes, Anatidae). Four adult male birds (two for each species) were collected from the Iraqi environment and used in the current study by buying them from the AL-Ghezil Market, a local market in Baghdad City. Anaesthetized, dissected, and laboratory work were conducted and visually documented. The results of the study showed that the tongue of both birds was characterized by the appearance of being a narrow-elongated organ divided into three areas represented by the apex, the lingual body, and the root. It was noted that there was a median sulcus, lingual prominence, and conical and filiform papillae of varying numbers and distribution on the dorsal surface of the tongue. While the ventral surface was free of lingual papillae, but it might contain in return a triangular squamous plate called a lingual nail or a bar-shaped structure called a lyssa in proportion to the type and nature of nutrition.

Behaviour frequencies, spatial distribution and social network of Grimaud geese during the laying season

Most domestic goose breeds were domesticated from the Greylag goose. Even though domestication resulted in a change in the production and behaviour of birds compared to their ancestors, geese are relatively recently domesticated and less intensively selected for production compared to other species (i.e. chickens or dairy cattle). In this respect, we hypothesised that the territorial defence of ganders would be present during the laying season similar to the wild ones. The behaviour of birds was expected to differ by sex, the month of the laying season and the time of the day. We assumed that the hierarchy within a group would be affected by the influence of other groups. Altogether 150 birds in three fifty-bird groups were examined over five observation events indicating the onset (December), the peak (the start, the middle and the end of January) and the end (the end of February) of the laying season. Twenty behaviour elements classified into five categories (locomotion, static behaviours, feed intake, comfort behaviours and social behaviours) were examined regarding the three groups as replicates. The territorial defence of ganders was evaluated by spatial distribution analysis, and the relations within groups were investigated by social network analysis. Between sexes, only the frequency of social behaviours presented differences. Ganders showed social behaviour more frequently than geese (10.89% vs 1.49%, P= 0.000). The frequency of static behaviours was the lowest at the onset of January and the highest in February (21.18% vs 38.03%, P= 0.000). The frequency of feed intake was the highest in December and the lowest in February (18.33% vs 4.83%, P= 0.000). Comfort behaviours showed the lowest frequency in December and the highest at the onset of January (36.65% vs 57.63%, P= 0.000). The social behaviours were unchanged in December and January but decreased in February (4.40–6.37% vs 1.16%, P=0.000). Only the frequency of social behaviours differed by the time of the day, indicating the highest frequency in the morning (55.60%, P=0.003). The results of the spatial distribution analysis did not strongly support the presence of territorial defence of ganders. The structure within a group was the most explicit in Group 1 having an adjacent large group and another small group by which it was likely to be influenced. It might be concluded that domestic geese ganders did not keep their territory-holding ability to a full extent during domestication and the behaviour (mainly social interactions) of domestic geese is influenced by sex, the month of the laying season and the time of the day. The structure within a group was somewhat influenced by the adjacent groups.

First molecular insights into gastrointestinal helminths of domestic birds in the Caspian Sea Littoral of Iran with an emphasis on the One Health concern

The current investigation was carried out during the period from July 2022 to March 2023, aiming to investigate the prevalence of gastrointestinal helminths in domestic birds collected from traditional markets in Guilan province. One hundred forty-eight domestic birds, including chickens (Gallus gallus domesticus), domestic ducks (Anas platyrhynchos domesticus), greylag geese (Anser anser), and domestic turkeys (Meleagris gallopavo domesticus) were examined. Totally, 42.56% of the investigated birds were positive for helminthic parasites. Morphological analysis revealed varying infection rates among birds: Echinostoma revolutum (5.40%), Hypoderaeum conoideum (2.02%), Cloacotaenia megalops (0.67%), Hymenolepididae family (4.05%), Ascaridia galli (16.89%), and Heterakis gallinarum (4.72%). The investigation involved molecular analysis of the 18S and ITS1 + 5.8S + ITS2 rRNA gene regions. The findings indicated that the 18S region of nematode isolates exhibited a similarity of 92 to 100% with sequences in the GenBank, whereas trematode and cestode isolates showed a gene similarity ranging from 88 to 99%. The ITS regions of nematode, trematode, and cestode isolates exhibited genetic similarities ranging from 87 to 100%, 73–99%, and 75–99%, respectively. Furthermore, phylogenetic analysis confirmed the categorization of the identified species within the Ascaridiidae, Heterakidae, Hymenolepididae, and Echinostomatidae families, indicating their close affinity with previously documented species. Implementing precise control measures such as consistent monitoring, adequate sanitation protocols, and administering anthelmintic treatments is crucial for effectively managing parasitic infections in free-range and backyard poultry farms. Additionally, conducting further surveys is advisable to assess the impact of these parasites on the health and productivity of poultry in the investigated area.

Investigations of the tick burden on passeriform, water-associated and predatory birds reveal new tick-host associations and habitat-related factors of tick infestation.

Previous studies on the tick infestation of birds in the Carpathian Basin focused on songbirds (Passeriformes). Thus, the primary aim of the present work was to extend the scope of previous studies, i.e. to include aquatic (water-associated) bird species in a similar context, especially considering that these birds are usually long-distance migrants. Between March 2021 and August 2023, 11,919 birds representing 126 species were checked for the presence of ticks. From 352 birds belonging to 40 species, 905 ixodid ticks were collected. Tick species were identified morphologically and/or molecularly. Ticks from avian hosts belonged to seven species: Ixodes ricinus (n = 448), I. frontalis (n = 31), I. festai (n = 2), I. arboricola (n = 36), I. lividus (n = 4), Haemaphysalis concinna (n = 382) and Dermacentor reticulatus (n = 2). Nymphs of I. ricinus occurred with a single activity peak around March-May, whereas its larvae typically infested birds in May, June or July. By contrast, H. concinna usually had its activity maximum during the summer (nymphs in June-July, larvae later in July-August). Interestingly, two ornithophilic species, I. frontalis and I. arboricola, were most active around winter months (between October and April). A significantly lower ratio of aquatic birds was found tick-infested than songbirds. Several new tick-host associations were revealed, including I. ricinus from Greylag Goose (Anser anser) and D. reticulatus from Great Egret (Ardea alba) and Sedge Warbler (Acrocephalus schoenobaenus). Ticks were collected for the first time in Europe from two species of predatory birds as well as from Little Bittern (Ixobrychus minutus). Bird species typically inhabiting reedbeds were most frequently infested with H. concinna, and most ticks localized at their throat, as opposed to forest-dwelling avian hosts, on which I. ricinus predominated and ticks were more evenly distributed. In the evaluated region, aquatic birds appear to be less important in tick dispersal than songbirds. However, newly revealed tick-host associations in this category attest to their hitherto neglected contribution. The results suggest that the habitat type will have significant impact not only on the species composition but also on the feeding location of ticks on birds.

Effective treatment of a severe neck wound in a greylag goose (Anser anser) using autologous thrombocyte‐rich plasma and xenogeneic fibrin mesh

AbstractThis report details the successful treatment of a severe neck wound in a feral greylag goose (Anser anser) using autologous thrombocyte‐rich plasma and xenogeneic fibrin mesh from a domestic dog. The goose's thrombocyte‐rich plasma was obtained after collecting 5 mL of blood, mixing it with an acid‐citrate‐dextrose solution, and centrifuging at 200 × g for 10 minutes. The lower third of the plasma, along with the buffy coat, was collected. For xenogeneic fibrin mesh, canine blood was centrifugated at 553 × g for 10 minutes, and the clot was extracted and pressed between culture dishes for 10 minutes to create a mesh and harvest serum. After treatment with thrombocyte‐rich plasma and the fibrin mesh, there was a notable improvement within 1 week, leading to complete healing after 2 weeks. This outcome exemplifies the promising potential of thrombocyte‐rich plasma and xenogeneic fibrin meshes in treating wounds in avian species.

Selection of a diversionary field and other habitats by large grazing birds in a landscape managed for agriculture and wetland biodiversity

Abstract Several populations of cranes, geese, and swans are thriving and increasing in modern agricultural landscapes. Abundant populations are causing conservation conflicts, as they may affect agricultural production and biodiversity negatively. Management strategies involving provisioning of attractive diversionary fields where birds are tolerated can be used to reduce negative impact to growing crops. To improve such strategies, knowledge of how the birds interact with the landscape and respond to current management interventions is key. We used GPS locations from tagged common cranes (Grus grus) and greylag geese (Anser anser) to assess how they use and select differentially managed habitats, such as diversionary fields to decrease impact on agriculture and wetlands protected for biodiversity conservation. Our findings show a high probability of presence of common cranes and greylag geese in the protected area and in the diversionary field, but also on arable fields, potentially causing negative impact on agricultural production and wetland biodiversity. We outline recommendations for how to improve the practice of diversionary fields and complementary management to reduce risk of negative impact of large grazing birds in landscapes tailored for both conservation and conventional agriculture.

5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algorithm

5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algorithm

Intrinsic and extrinsic factors modulating vigilance and foraging in two gregarious foragers

Abstract A continuous balance between costs and benefits dictates individual vigilance and foraging dynamics. In group-living animals, understanding the resulting trade-off is often complicated by multiple confounding effects. Vigilance and foraging levels may be the result of intrinsic (e.g., body size, trophic ecology, migratory phenology) and extrinsic (e.g., flock size, edge effect, group dynamism) factors, potentially differing between species, individuals, and contexts. We explored this idea by investigating intrinsic and extrinsic factors influencing vigilance and foraging behavior of two sympatric gregarious bird species that differ markedly in body size and foraging strategies (Greylag Goose Anser anser and Common Crane Grus grus), during their non-breeding period. Interspecific differences were detected in activity allocation and in response to group-related variables. For both species, time spent in vigilance decreased with increasing flock size and with increasing distance from the edge of the group. While cranes allocated the resulting time to foraging, the same did not occur in geese. Changes in individual position in the group (i.e., peripheral vs. central or vice versa) elicited a prompt behavioral change (i.e., vigilance vs. foraging or other activity). Temporal changes in activity budgets were reported for geese but not for cranes, with a decrease of vigilance and an increase of foraging as winter progressed. Results allowed to disentangle the role of multifactorial determinants of vigilance and foraging, in turn increasing our understanding of underlying forces driving the evolution of behavioral traits and of group-living.

Load Margin Assessment of Power Systems using Recurrent Neural Network and Greylag Goose Optimization

The complexity of operating power systems requires the development of methods capable of assisting in system monitoring. The Voltage Stability Margin or Load Margin (LM) represents an index that informs how much the system’s load level can increase without causing a case of instability. The LM is determined using voltage stability criteria, but low-frequency modes can arise with increasing load and can cause system instability. This article proposes a method based on Recurrent Neural Networks (RNNs) for monitoring LM. Furthermore, a metaheuristic called Greylag Goose Optimization is applied to choose the system buses whose measurements will be RNN inputs.