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Bird‐related outages greatly threaten the safety of overhead transmission and distribution lines, while electrocution and collisions of birds with power lines, especially endangered species, are significant environmental concerns. Automatic bird recognition can be helpful to mitigate this contradiction. This paper proposes a method for automatic classification of bird species related to power line faults combining deep convolution features with error‐correcting output codes support vector machine (ECOC‐SVM). An image dataset of about 20 high‐risk and 20 low‐risk bird species was constructed, and the feed‐forward denoising convolutional neural network was used for image preprocessing. The deep convolution features of bird images were extracted by DarkNet‐53, and taken as inputs of the ECOC‐SVM for model training and bird species classification. The gradient‐weighted class activation mapping was used for visual explanations of the model decision region. The experimental results indicate that the average accuracy of the proposed method can reach 94.39%, and its performance was better than other models using different feature extraction networks and classification algorithms. [ABSTRACT FROM AUTHOR]

With increasing demands for biodiversity monitoring, the integration of camera trapping (CT) and deep learning automation holds significant promise. However, few studies have addressed the application potential of this approach in urban areas in Asia. 4064 CT images targeting 18 species of urban wildlife in South Korea were collected and used to fine-tune a pre-trained object detection model. The performance of the custom model was evaluated across three levels: animal filtering, mammal and bird classification, and species classification, to assess its applicability. A comparison with existing universal models was conducted to test the utility of the custom model. The custom model demonstrated approximately 94% and 85% accuracy in animal filtering and species classification, respectively, outperforming universal models in some aspects. In addition, recommendations regarding CT installation distances and the acquisition of nighttime data were provided. Importantly, these results have practical implications for terrestrial monitoring, especially focusing on the analysis of local species. Automating image filtering and species classification facilitates efficient analysis of large CT datasets and enables broader participation in wildlife monitoring. [ABSTRACT FROM AUTHOR]

Recently, there has been increased focus on the origins and history of common names for organisms, especially birds. Of particular interest are eponymous common names that reflect our colonial past. While identification of alternative names can be straightforward for some species, for those that migrate across jurisdictions including the lands of multiple Traditional Owner/Indigenous groups, reaching consensus on a single name that reflects the features of the species and their cultural importance can be substantially more complex. Using the migratory Ardenna carneipes as a case study, we propose a new common name (Sable Shearwater) for the species and discuss the many challenges that others will need to consider when navigating this important yet sensitive space. [ABSTRACT FROM AUTHOR]

In this study, data presented of birds of prey (Accipitriformes, Falconiformes) specimens deposited in the collection of the Šariš Museum in Bardejov (SMB), Slovakia. The work builds on previous studies highlighting the importance of the SMB museum collections, but also the importance of the museum on a global scale. In total, data of 437 individuals belonging to 22 species from 86 sites in eastern Slovakia were evaluated. Individuals were acquired in 1954–1985 (and two specimens of Buteo buteo in 1998), but mainly in 1962 (n = 63, 14.4%) and 1963 (n = 32, 7.3%). Moreover, one specimen of the Common Buzzard (B. buteo) had signs of „a dilution (isabel)" and one bird of the Western Marsh Harrier (Circus aeruginosus) had signs of „melanism". The importance of the work also includes cataloguing, which provides important data on regional museums and their collections that can be significant not only regionally but also globally. Moreover, in the SMB collections there are also 20 specimens of 15 species of exotic Accipitriformes and Falconiformes [1 ex. – Accipiter striatus; 1 ex. – Buteo buteo; 1 ex. – B. ventralis; 1 ex. – Buteogallus gundlachii; 1 ex. – B. meridionalis; 1 ex. – Caracara plancus; 1 ex. – Elanoides forficatus; 5 ex. – Falco sparverius; 1 ex. – Geranoaetus polyosoma; 1 ex. – Gyps africanus; 1 ex. – Haliaeetus albicilla; 2 ex. – Ictinia plumbea; 1 ex. – Pandion haliaetus; 1 ex. – Rupornis magnirostris; 1 ex. – Vultur gryphus] originating mainly from Argentina and Cuba as well from Africa and Hungary. [ABSTRACT FROM AUTHOR]

The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet technique was utilized for the investigation. The study adapts the fine-tune model to learn the pertinent patterns from mel spectrogram images specific to this bird species classification task. The fine-tuned EfficientNet model combined with a type of Recurrent Neural Networks (RNNs) namely Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). RNNs are employed to capture the temporal dependencies in audio signals, thereby enhancing bird species classification accuracy. The dataset utilized in this work contains nearly 17,000 bird sound recordings across a diverse range of species. The experiment was conducted with several combinations of EfficientNet and RNNs, and EfficientNet-B7 with GRU surpasses other experimental models with an accuracy of 84.03% and a macro-average precision score of 0.8342. [ABSTRACT FROM AUTHOR]

With the development of air transport and improved ecological environment, bird strikes on aircraft have become a significant threat to flight safety. Accurate and efficient identification of airport birds can enhance the efficiency of airport staff in bird strike prevention. Public datasets for bird classification are typically limited in size and not specifically tailored for airport birds. Therefore, this paper introduces a self‐built airport bird dataset, ADB‐20, and proposes an image recognition method based on an improved ResNet model. First, this paper replaces traditional convolution in the residual structure with the Pyramidal Convolution method, ensuring the extraction of multiscale features. Second, it introduces the Parallel Convolutional Block Attention Moduleto the backbone network, considering features in both channel and spatial dimensions. Last, the Atrous Spatial Pyramid Pooling module is incorporated to capture contextual information at various scales. Experimental results show that the PPA‐ResNet model achieves an accuracy of 95.2%, with a recall of 93.7%, a precision of 96.8%, and an F1 score of 95.2% on the ADB‐20 dataset. The proposed algorithm significantly enhances classification performance compared to other mainstream image classification algorithms. These indicators confirm that the results of this study can precisely identify airport birds, aid airport personnel in making informed decisions, and guarantee the safety of aviation transport. [ABSTRACT FROM AUTHOR]

The accessibility of genomic tools in evolutionary biology has allowed for a thorough exploration of various evolutionary processes associated with adaptation and speciation. However, genomic studies in natural systems present numerous challenges, reflecting the inherent complexities of studying organisms in their native habitats. The utilization of museum specimens for genomics research has received increased attention in recent times, facilitated by advancements in ancient DNA techniques. In this study, we have utilized a museum genomics approach to analyze historic specimens of Woolly-necked storks (Ciconia spp.) and examine their genetic composition and taxonomic status and explore the evolutionary and adaptive trajectories of populations over the years. The Woolly-necked storks are distributed in Asia and Africa with a taxonomic classification that has been a matter of ambiguity. Asian and African Woollynecks were recently recognized as different species based on their morphological differences; however, their genomic validation was lacking. In this study, we have used ∼70-year-old museum samples for whole-genome population-scale sequencing. Our study has revealed that Asian and African Woollynecks are genetically distinct, consistent with the current taxonomic classification based on morphological features. However, we also found a high genetic divergence between the Asian subspecies Ciconia episcopus neglecta and Ciconia episcopus episcopus , suggesting this classification requires a detailed examination to explore processes of ongoing speciation. Because taxonomic classification directly impacts conservation efforts, and there is evidence of declining populations of Asian Woollynecks in Southeast Asia, our results highlight that population-scale studies are urgent to determine the genetic, ecological, and phylogenetic diversity of these birds. [ABSTRACT FROM AUTHOR]

This article is a book review of "The Vulnerability and Transformation of Indonesian Peatlands: Navigating Social-Ecological Tipping Points for Resilient Futures." The book explores the degradation and transformation of peatlands in Indonesia, focusing on the ecological, social, and economic impacts. It discusses the historical resilience of these landscapes and the current challenges they face, such as deforestation, fires, and land conversion. The book also examines efforts to restore peatlands and promote sustainable management. It provides insights into the complex dynamics of peatland disturbances and offers guidance for adaptation and transformation in the face of ongoing crises. [Extracted from the article]

We need landscape-scale approaches to design and manage agro-ecosystems that can sustain both agricultural production and biodiversity conservation. In this study, yield figures provided by 299 farmers served to quantify the energyequivalents of food production across different crops in 49 1-km2 landscapes. Our results show that the relationship between bird diversity and food energy production depends on the proportion of farmland within the landscape, with a negative correlation observed in agriculture dominated landscapes (≥ 64-74% farmland). In contrast, neither typical farmland birds nor butterflies showed any significant relationship with total food energy production. We conclude that in European temperate regions consisting of small-scale, mixed farming systems (arable and livestock production), productivity and biodiversity conservation may not be purely antagonistic, particularly when (semi-)natural habitats make up a large fraction of the landscape (≥ 20%). [ABSTRACT FROM AUTHOR]

Bird populations respond rapidly to environmental change making them excellent ecological indicators. Climate shifts advance migration, causing mismatches in breeding and resources. Understanding these changes is crucial to monitor the state of the environment. Citizen science offers vast potential to collect biodiversity data. We outline a project that combines citizen science with AI-based bird sound classification. The mobile app records bird vocalisations that are classified by AI and stored for re-analysis. Additionally, it shows a shared observation board that visualises collective classifications. By merging long-term monitoring and modern citizen science, this project harnesses the strength of both approaches for comprehensive bird population monitoring. [ABSTRACT FROM AUTHOR]

Background: Understanding how landscape characteristics affect animal movement is essential for conservation in human-dominated habitats. A fundamental question is how monoculture agroforests, including rubber and tea plantations, affect wildlife and its movement. Experimental translocations represent an important technique to assess animals' habitat selection while moving through agricultural matrices, especially when complemented with observations of birds' natural movements, and with "control" translocations, in which birds are moved within their natural habitat such as forest. Yet, experimental translocations have been little used for birds outside the Western Hemisphere. Methods: We conducted experimental translocations and home-range measurements on an understory forest specialist, Brown-capped Babbler (BCBA, Pellorneum fuscocapillus), and a forest generalist, Tickell's Blue Flycatcher (TBFL, Cyornis tickelliae). These species were studied in three rubber plantations, which also included some open areas mostly planted with tea, and in three forest reserves of Sri Lanka. Results: Four of the five BCBAs translocated within disturbed habitats (rubber plantations) could not return to their capture locations. However, all four individuals within undisturbed habitats (forest reserves) successfully returned to their point of origin within 10.5 daytime hours. In contrast, all TBFLs returned to their capture locations in both disturbed (n = 7) and undisturbed habitats (n = 3) within 11.3 daytime hours. A Cox-proportional survival model demonstrated that the percentage of rubber cover decreased return time, similar to the effect of open-area cover. The home range surveys (n = 13 for BCBA, n = 10 for TBFL) revealed that very little of the birds' natural home-ranges was covered by rubber (0.2% for BCBA, 13.1% for TBFL at 50% Kernel Density Estimates KDE). Home range size for BCBA was approximately half the size in disturbed habitats compared to undisturbed ones, although there was no significant difference between habitats for TBFL. Conclusions: We conclude that rubber plantations can be impermeable to understory habitat specialist birds, and even generalist species may avoid them long-term. Our findings highlight the potential utility of strips of native vegetation, particularly those featuring understory layers, as corridors to facilitate the movement of forest specialists in landscapes dominated by rubber plantations and other types of disturbed habitats. [ABSTRACT FROM AUTHOR]

Research into bird habitat selection can unveil the impact of specific habitat characteristics on bird survival. However, empirical information on the environmental factor preferences and differences within the yew ecological garden for birds is still lacking. This study was conducted in a yew ecological garden located in the southern experimental area of the Meihua Mountain National Nature Reserve, Fujian Province, China. We selected three commonly occurring bird species for analysis to discern the factors influencing habitat selection and interspecific differences and the impact of habitat environmental factors. Canopy density and coverage were identified as dominant factors influencing habitat selection for the Red-billed Blue Magpie (Urocissa erythrorhyncha), Black Bulbul (Hypsipetes leucocephalus), and Orange-bellied Leafbird (Chloropsis hardwickii). The height of perching trees, average tree height, and average diameter at breast height were all important determinants for the habitat selection of Red-billed Blue Magpie and Black Bulbul. These dominant factors were consistent with logistic regression analysis results. Both one-way ANOVA and stepwise discriminant analysis revealed significant differences in habitat selection among three commonly occurring bird species. Our results provide important insights into avian habitat selection in patchy habitats, which can be used as a guideline for future conservation and habitat management. [ABSTRACT FROM AUTHOR]

Few studies have been made of the specific importance of waterfowl as food in the Baltic Sea during the late medieval and early modern period. This study shows the seasonal importance of water birds to island and coastal inhabitants living in the middle of the Baltic Sea during the late medieval and early modern periods (ca. CE 1400–1700) and practical implementations for interpretation of cut and chop marks. The paper is based on a bird bone assemblage retrieved from a remote ecclesial site on the island group of Kökar, which is part of the Åland Islands, as well as historical documents and local specialist knowledge. The site was established by Franciscan friars somewhere in the mid‐15th century. Documents indicate that the friars had landownership, income from taxing nearby fisheries, and the right to seal tithe. These assets were taken over by the crown in the mid‐16th century due to the Reformation and a parish church was established on the same site after the friary was dissolved. The bone assemblages from the site include birds, mammals, and fish. The material shows a relatively small number of chicken bones in relation to waterfowl, allowing one to look closer into the structural food economy around water birds for the area. The bones also show several cut and chop marks, which have been analyzed to explore the possible processing of these birds. The results show that fowling was concentrated to a couple of species most likely during spring when these species were abundant. The placement of the cut and chop marks indicates that a large number of birds were likely processed at the same time. [ABSTRACT FROM AUTHOR]

This study aims to analyze the threat of edge effects on endemic animal populations due to the presence of causeways in the TNGHS corridor habitat. Indicators of the occurrence of edge effects are characterized by the further distance of wildlife populations from sources of disturbance such as noise, light, and frequency of human activity mechanisms. Belt transect research method for population study and distribution of Javan Gibbon and Surili. Intensive Point Account (IPA) to measure the diversity index of bird species and Javan eagles. Encounter Rate is used for animal distribution studies. Results On the observation transect made along 3 km, the population density of Javan Gibbons was obtained 0.114 ind/ha, Surili 0.024 indv/ha, and Monkey 0.028 indv/ha. Bird species diversity index in 3 habitat patches each Purasari H'= 2.78 with 38 species; Garehong with 27 species, and Corridor H'= 3,087 with 27 species. The similarity of species is that there are only 7 species of birds found in the 3 habitats of the patch. As a result of primate population density and burng species similarity, it can be concluded that the cross-corridor access road area has experienced an edge effect and tends to be a bottle-neck area for the distribution of animals, especially primates. [ABSTRACT FROM AUTHOR]

This article provides a preliminary overview of the species of wild birds that lived in the fourteenth-century County of Holland, now the Netherlands, on the basis of archaeological and historical sources. It argues that scholars should devote more attention to the Late Middle Ages (1300-1500) as a historical baseline for the study of biodiversity, and demonstrates the value of using medieval financial administration (accounts) as a source for such research. The article identifies 46 species of birds, most of which had substantial socio-economic value (birds of prey, wildfowl, herons and spoonbills). Because some bird populations were actively managed to secure a steady supply, it is possible to gain insight into historical population dynamics. This study can also serve as an example in designing similar research on other species and geographical regions. [ABSTRACT FROM AUTHOR]

Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. We developed an annotated dataset for a suite of marine birds from high-resolution digital aerial imagery collected over open water environments to train the models. The proposed 3-stage workflow for automated, in-flight data processing includes: 1) image filtering based on the probability of any bird occurrence, 2) bird instance detection, and 3) bird instance classification. For image filtering, we compared the performance of a binary classifier with Mask Region-based Convolutional Neural Network (Mask R-CNN) as a means of sub-setting large volumes of imagery based on the probability of at least one bird occurrence in an image. On both the validation and test datasets, the binary classifier achieved higher performance than Mask R-CNN for predicting bird occurrence at the image-level. We recommend the binary classifier over Mask R-CNN for workflow first-stage filtering. For bird instance detection, we leveraged Mask R-CNN as our detection framework and proposed an iterative refinement method to bootstrap our predicted detections from loose ground-truth annotations. We also discuss future work to address the taxonomic classification phase of the envisioned workflow. [ABSTRACT FROM AUTHOR]

The Langbian Plateau, a biodiversity hotspot in the southern Annamites of Viet Nam, has undergone extensive hunting pressure. However, the limited information on the effects of overexploitation on the current status and community composition of wildlife hinders effective conservation efforts, including the implementation of targeted patrols to reduce snaring. In this study, we conducted a camera‐trapping survey across the Langbian Plateau, consisting of a broadleaf evergreen and coniferous habitat mosaic. We recorded 46 ground‐dwelling mammals and birds, including several threatened Annamite endemics. Using multi‐species Royle‐Nichols model and landscape covariates, we found higher richness in broadleaf evergreen forest located in more remote and less rugged areas. We then used species responses to covariates to predict species distribution and identify high‐priority areas for conservation. Furthermore, we constructed diversity profiles that indicated higher biodiversity in broadleaf evergreen forest compared to the coniferous forest. Finally, we used a dissimilarity index to assess the level of defaunation, revealing 16% of the community had been lost, with higher levels of defaunation for threatened and larger‐sized species. Our findings provide insights into the status, distribution, and occurrence of the ground‐dwelling mammal and bird communities in the Langbian Plateau, and can help stakeholders design more effective conservation strategies to protect existing populations. [ABSTRACT FROM AUTHOR]

This article provides a list of recently published books and articles on various topics related to Rhode Island history. It covers subjects such as skiing in Rhode Island, the Brown University Band, Rhode Island election tickets, birds of Rhode Island, the Civilian Conservation Corps camps, Newport's Catholics, Puritanism in England and America, and constitutionalism in the American Revolution. The article aims to give library patrons a brief overview of these resources for further research. Additionally, there is an article titled "Timeline of the Formation and Service of the 1st Rhode Island Regiment - Part I" that offers a detailed timeline of the activities and formation of the 1st Rhode Island Regiment from October 1777 to December 1778. It includes an overview essay by the website's creators, a video interview with historian James Oakes, and numerous primary source documents. This resource is particularly valuable for researchers interested in the early years of the American Revolution, with a focus on the experiences of African and Indigenous soldiers. [Extracted from the article]

Classification of birds is the area that is quite complex and involved such a lot of varieties that demands the correct as well as the professional organization of the information. This study addresses the problem of semantic interoperability in the bird categorization process by means of constructing an ontology which covers all the words used in taxonomic descriptions. The paper exploits XML/RDF standards for semantic web compatibility and Open Link Virtuoso SPARQL Query Editor that enables simple interaction with its tools and visualization in the process of querying and presentation of results. Attempt to develop a specific ontology for bird identification for the purpose of optimizing the accessibility and retrieval of heterogeneous bird-oriented data that ultimately help in building effective knowledge management system in this field. The effectiveness of an ontology is evaluated by its ability to make possible classification of diverse information. This research methodology is proposed for implementation in a broad range of researches, education, and conservation programs that can targeted to enhance their output and increase accuracy. [ABSTRACT FROM AUTHOR]

Background: Spatially explicit simulation models of animal movements through the atmosphere necessarily require a representation of the spatial and temporal variation of atmospheric conditions. In particular, for movements of soaring birds that rely extensively on vertical updrafts to avoid flapping flight, accurate and reliable estimation of the vertical component of wind is critical. The interaction between wind and complex terrain shapes both the horizontal and vertical wind fields, highlighting the need to model the coupling between local terrain features and atmospheric conditions at scales relevant to animal movement. Methods: In this work, we propose a new empirical model for estimating the orographic updraft field. The model is developed using computational fluid dynamics simulations of canonical atmospheric conditions over moderately complex terrain. To isolate buoyancy and thermal effects, and focus on terrain-induced effects, we use only simulations of a neutrally stratified atmosphere to develop the model. The model, which we name Engineering Vertical Velocity Estimator (EVVE), is simple to implement and is a function of the underlying terrain elevation map, the desired height above ground level (AGL), and wind conditions at a reference height (80 m). We validate the model with data from the Alaiz mountain (Spain) field campaign. Results: Compared to observations, the proposed improved model estimates the updrafts at 120 m AGL with a mean error of 0.11 m/s ( σ = 0.28 m/s), compared to 0.85 m/s ( σ = 0.58 m/s) for its baseline. For typical land-based wind turbine hub heights of 80 m AGL, the proposed model has a mean error of 0.04 m/s ( σ = 0.25 m/s), compared to baseline 0.54 m/s ( σ = 0.45 m/s) estimations. We illustrate an application of the model in movement ecology by comparing simulated tracks and presence maps of golden eagles (Aquila chrysaetos) moving across two distinct landscapes. The tracks and presence maps are obtained using a simple heuristic-based movement model, with the updraft field given by the proposed model and a wind vector-based estimation approach that is currently in wide use in movement ecology studies of raptors and other soaring birds. Conclusions: We highlight that movement model results can be sensitive to the underlying orographic updraft model, especially in studies of fine-scale movements in regions of complex topography. We suggest adopting the proposed model rather than the wind vector estimation method for studies of soaring bird movements. [ABSTRACT FROM AUTHOR]

The accurate identification of bird image is of great significance to protecting the ecological environment and bird species diversity. To address the issue of low recognition accuracy arising from the similarities in features among different bird species and the susceptibility of shallow edge features to loss, this paper proposes a fine-grained bird image classification model that incorporates hierarchical feature fusion and counterfactual feature selection. The model is based on vision transformer and builds a hierarchical feature fusion module and a counterfactual feature enhancement module. The hierarchical feature fusion module superimposes shallow features rich in fine-grained information into deep features to improve the problem of lack of edge detail information in key features. The counterfactual feature enhancement module selects distinguishing features through counterfactual intervention to reduce classification errors caused by highly similar features of different species of birds. The experimental results show that the method can achieve 91.9 % and 91.4 % accuracy on two available datasets, CUB-200-2011 and NABirds, respectively, which is higher than the current mainstream fine-grained bird recognition algorithms and shows excellent classification performance. [ABSTRACT FROM AUTHOR]

The article offers information on the bipartisan effort behind the Migratory Bird Treaty Act (MBTA) of 1918, a landmark wildlife conservation law. Topics discussed include the drastic decline in bird populations due to hunting and fashion; Senator George P. McLean's coalition-building for bird protection; and the potential for future bipartisan solutions to modern challenges through generational change.

The problem of classification of different species of birds in the images is relevant in the modern world. The essence is to automatically determine the species of birds depicted in the photo using artificial intelligence. It is important for several areas of human life, in particular for nature conservation, environmental research, education, and ecotourism. The development of a classifier, based on deep learning methods, can help to effectively solve the problem of classifying different species of birds, ensuring high accuracy. The work developed a new effective algorithm for the classification of different species of birds in the images. An optimal model architecture was built using the transfer learning approach. A dataset of 525 bird species was analyzed and preprocessed in detail. The model training process was carried out, which includes two phases: only the upper layers are deactivated and the last 92 layers of the pretrained EfficientNetB5 model (not including BatchNormalization layers) and the top layers are activated. As a result, efficacy indicators were obtained: accuracy = 98.86%, precision = 0.99, recall = 0.99, and F1 score = 0.99, which showed improvement in comparison with modern research. The developed classifier is best suited for such areas of human life as education and ecotourism because for them the number of different species of birds that the classifier can determine is very important. [ABSTRACT FROM AUTHOR]

Humans have a particularly strong connection with birds, driving the enormous popularity of residential bird feeding in much of the world.We conducted a web search to document US state wildlife management agency responses to two recent avian disease outbreaks, finding that 23 agencies made recommendations to cease feeding wild birds in 2021–2022.The psychological benefits of bird feeding for humans are well‐documented but often overlooked in management decisions in response to avian disease outbreaks.Likewise, ecological evidence does not necessarily support ceasing bird feeding to reduce the spread of every avian disease.Ecological and social science need to be applied in tandem to ensure that well‐intended guidance to cease feeding of birds does not have unintended consequences. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

In order to improve conservation outcomes translocation or reintroduction of individuals may be necessary. When song learning birds are translocated, changes in the cultural diversity of song repertoires, or abnormal vocalizations, in the new population can be a problem. We monitored song production over 8 years in a reintroduced population of the cirl bunting (Emberiza cirlus). Chicks were removed from nests in Devon, UK, between 2006 and 2011, translocated at 6 days old to be hand‐reared and released in Cornwall, UK. Recordings at the release site in 2011 showed a significantly reduced population repertoire and individuals sang abnormal song types compared to the source populations in Devon. However, recordings in 2019, showed population song repertoire had reached the level of source populations of similar size, and song types were species typical. Our study shows that species can recover from a cultural bottleneck and suggests that, for some song learning birds, if translocation of nestlings is necessary it may not lead to long‐term problems for communication and thus population persistence. For future translocations of nestlings, we recommend that efforts are made to provide tutoring to enable song learning. This may be achieved by providing recordings, but may also include providing adult song tutors. In addition, playback of "normal" songs to translocated populations may aid in development of species typical song repertoires, although care must be taken that this is not disturbing the reintroduced birds. [ABSTRACT FROM AUTHOR]

The British Ornithologists' Union Records Committee (BOURC) has released its 56th report, which covers the period from October 2022 to September 2023. The report announces changes to the British List, including the addition of the Kelp Gull, Least Bittern, and Sulphur-bellied Warbler. It also discusses the inclusion of subspecies like the Eider and Greenfinch. The report addresses the reconsideration of the status of the White-headed Duck on the British List, which remains in Category D. Furthermore, the report highlights the changes made to the British List following the adoption of versions 13.1 and 13.2 of the IOC World Bird List. The text explores various topics related to bird species and their identification, including the use of molecular genetic and bioacoustic data in bird taxonomy. It also mentions the use of technology, such as autonomous sensors, in monitoring bird populations. The text provides updates on specific bird species being considered for inclusion in the British List and the total number of species on the list. The text concludes with acknowledgments and a list of committee members and consultants. [Extracted from the article]

Abstract—To reveal the features of the distribution of birds in Northern Eurasia, the results of surveys carried out in the period from 1880 to 2019 (with interruptions and mainly since 1960) averaged over the first half of summer (May 16–July 15) were analyzed. Three hundred and fifty-four researchers participated in the collection of material (over 110 years). Data processing was carried out using multivariate statistics methods, including cluster analysis and linear qualitative approximation of the connection matrices. The classification of bird species by their distribution accounts for 42% of its similarity. The information content of representations decreased by only 10–15% (by 12%, on average) with twice as many species analyzed, with a significantly larger number of surveyed habitats and area of the studied territory (as compared to the previously surveyed East European and West Siberian plains and Altai). This level of explanation can be considered satisfactory (the correlation coefficient is 0.65). The summer distribution of bird species, as well as the heterogeneity of their distribution on the whole, is determined by changes in the hydrothermal regime in the zonal belt and provincial aspects. The heat-to-moisture ratio determines the type of vegetation and its productivity both on land and in aquatic and semiaquatic habitats. The specificity of the vegetation type in territories and water areas, taking into account the anthropogenic transformation, coincides with the heterogeneity of the bird distribution and the formation of ornithocomplexes as a whole. With the division of geographical space into zones, subzones, and especially physical-geographic countries, the variability in the distribution of birds and their communities is associated to a lesser extent, occupying the second and third places in the hierarchy of significance, respectively. [ABSTRACT FROM AUTHOR]

Hunting and illegal bird killing are major concerns for avian conservation globally. Unsustainable bird hunting in wetland habitats, particularly those located along important flyways, has contributed to avifauna defaunation and species endangerment. Since wetlands are primary habitats for migratory birds and are associated with anthropogenic landscapes, a comprehensive understanding of bird harvesting, and its drivers is fundamental to reduce threats to current avifauna. In this study, we examined the distribution of illegal bird hunting and its socioecological drivers in the Indawgyi wetland ecosystem in Myanmar by integrating data from bird surveys, household surveys, and market surveys. We found that illegal bird hunting using nets, traps, and poisonous substances is prevalent in areas close to water and during the migration season. People who had negative attitudes toward avian species were more likely to engage in bird hunting, primarily because of the conflicts between bird conservation and crop production. Socioeconomic needs were not the major driving factors of bird killing. We therefore suggest sustainable management interventions promoting coexistence by integrating bird conservation and agricultural production, accompanied by increasing awareness to improve avian conservation in an internationally important wetland in Myanmar. [ABSTRACT FROM AUTHOR]

This study aims to improve the classification accuracy of birdsongs by selecting the most pertinent features. This is important because birds are exceptional environmental regulators, but many species are endangered. The community can be assisted in distinguishing bird species and conserving the local environment if the classification is more precise. Nevertheless, because of disruptive noise and unfavorable qualities in the whispering of these bird species, feature selection focuses on enhancing performance accuracy. The use of the gray wolf optimizer (GWO) technique has been employed to identify the most optimum features from the data after outlier removal by the application of k-means clustering, reducing noise through YAMNet, and feature synthesis using gammatone cepstral coefficients (GFCC). This work utilizes the GWO algorithm to address the constraint management challenges associated with high-dimensional data in birdsong classification. The fitness functions used in this research are derived from the K-nearest neighbors (KNN) algorithm. The objective is to devise innovative ways for effectively managing constraints in the context of high-dimensional data. The number of features was reduced by more than 30.7% compared to the initial number of features and obtained an accuracy of 81.06%, as determined by the evaluation outcomes. This discovery improves precision by 4% and surpasses prior research. In summary, this work showcases the effectiveness of the optimization method, especially of GWO. It makes a valuable contribution to advancing a new workflow for analyzing high-dimensional data, specifically in enhancing the classification of birdsongs. [ABSTRACT FROM AUTHOR]

The article discusses a new deep learning AI tool called ECOGEN, which has been developed to enhance the identification of bird species in acoustic monitoring. The researchers found that adding artificial birdsong samples generated by ECOGEN to a birdsong identifier improved the classification accuracy by 12% on average. This tool aims to assist ecologists in monitoring and protecting sensitive and rare bird species in the wild, and it has the potential to be applied to other species that produce sounds, such as mammals, fishes, and amphibians. [Extracted from the article]

Bird voice classification is a crucial issue in wild bird protection work. However, the existing strategies of static classification are always unable to achieve the desired outcomes in a dynamic data stream context, as the standard machine learning approaches mainly focus on static learning, which is not suitable for mining dynamic data and has the disadvantages of high computational overhead and hardware requirements. Therefore, these shortcomings greatly limit the application of standard machine learning approaches. This study aims to quickly and accurately distinguish bird species by their sounds in bird conservation work. For this reason, a novel concept-cognitive computing system (C3S) framework, namely, PyC3S, is proposed for bird sound classification in this paper. The proposed system uses feature fusion and concept-cognitive computing technology to construct a Python version of a dynamic bird song classification and recognition model on a dataset containing 50 species of birds. The experimental results show that the model achieves 92.77% accuracy, 92.26% precision, 92.25% recall, and a 92.41% F1-Score on the given 50 bird datasets, validating the effectiveness of our PyC3S compared to the state-of-the-art stream learning algorithms. [ABSTRACT FROM AUTHOR]

Fifty-two species of birds belonging to 29 families were recorded in Pakke Tiger Reserve, Arunachal Pradesh, India. The highest number of species belonged to the family Muscicapidae with eight, Accipitridae, Bucerotidae, Columbidae, Phasianidae, and Pycnonotidae each with three species, while Ardeidae, Campephagidae, Falconidae, Megalaimidae, Phylloscopidae and Scolopacidae with two species each. The remaining seventeen families viz., Alcedinidae, Charadriidae, Chloropseidae, Coraciidae, Corvidae, Dicruridae, Eurylaimidae, Irenidae, Laniidae, Locustellidae, Motacillidae, Oriolidae, Phalacrocoracidae, Picidae, Strigidae, Sturnidae, and Vangidae with one species were recorded. IUCN (International Union for Conservation of Nature) conservation status of two species (Rhyticeros undulatus and Buceros bicornis), is categorised under VU (Vulnerable), two species Vanellus duvaucelii and Treron phayrei under NT (Near Threatened) while the remaining are categorized under LC (Least Concern). [ABSTRACT FROM AUTHOR]

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It is a long-standing view that the main mechanism maintaining narrow migratory divides in passerines is the selection against intermediate and suboptimal migratory direction, but empirical proof of this is still lacking. We present novel results from a willow warbler migratory divide in central Sweden from where birds take the typical SW and SE as well as intermediate routes to winter quarters in Africa. We hypothesized that individuals that take the intermediate route are forced to migrate in daytime more often when crossing wide ecological barriers than birds that follow the typical western or eastern flyways. Analyses of geolocator tracks of willow warblers breeding across the entire Sweden, including the migratory divide, provided no support for our hypothesis. Instead, birds that migrated along the western flyway were the most likely to undertake full day flights. The probability of migrating for a full day when crossing major barriers declined linearly from west to east. We speculate that this difference is possibly caused by more challenging conditions in the western part of the Sahara Desert, such as the lack of suitable day-time roost sites. However, it may equally likely be that willow warblers benefit from migrating in daytime if favorable tailwinds offer assistance. [ABSTRACT FROM AUTHOR]