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The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems. [ABSTRACT FROM AUTHOR]

Background: Studying the composition rules and evolution mechanisms of genome sequences are core issues in the post-genomic era, and k-mer spectrum analysis of genome sequences is an effective means to solve this problem. Result: We divided total 8-mers of genome sequences into 16 kinds of XY-type due to XY dinucleotides number in 8-mers. Previous works explored that the independent unimodal distributions observed only in three CG-type 8-mer spectra, while non-CG type 8-mer spectra have not the universal phenomenon from prokaryotes to eukaryotes. On this basis, we analyzed the distribution variation of non-CG type 8-mer spectra across 889 animal genome sequences. Following the evolutionary order of animals from primitive to more complex, we found that the spectrum distributions gradually transition from unimodal to tri-modal. The relative distance from the average frequency of each non-CG type 8-mers to the center frequency is different within a species and among different species. For the 8-mers contain CG dinucleotides, we further divided these into 16 subsets, where each 8-mer contains both CG and XY dinucleotides, called XY1_CG1 subsets. We found that the separability values of XY1_CG1 spectra are closely related to the evolution and specificity of animals. Considering the constraint of Chargaff's second parity rule, we finally obtained 10 separability values as the feature set to characterize the evolution state of genome sequences. In order to verify the rationality of the feature set, we used 14 common classification algorithms to perform binary classification tests. The results showed that the accuracy (Acc) ranged between 98.70% and 83.88% among birds, other vertebrates and mammals. Conclusion: We proposed a credible feature set to characterizes the evolution state of genomes and obtained satisfied results by the feature set on large scale classification of animals. [ABSTRACT FROM AUTHOR]

The family Eirenidae is one of the major taxa of the order Leptothecata, comprising approximately 80 species from ten genera. In this study, taxonomic investigations, including morphological observations and molecular 16S phylogenetic analyses, were conducted on unknown Eirenidae specimens collected off the coast of Iwaki, Fukushima Prefecture, eastern Japan, in June 2022. The specimens had the following morphological characteristics: marginal warts and tentacular bulbs with lateral cirri and without adaxial papillae, a mouth with simple lips, four simple radial canals, and eight statocysts common to the genus Eutima. However, this species can be distinguished from other species of Eutima by the number of tentacles, number and shape of marginal warts, position of the gonads, and gastric peduncle length. Moreover, the monophyly of the species was evident in the 16S rRNA phylogenetic tree (as indicated by the high bootstrap value of 100%), thereby supporting the validity of the new species. Based on these results, we describe it as a new species, Eutima onahamaensis, for taxonomic stabilization. We also made detailed observations of the morphology and molecular phylogenetic analyses of one of the species newly recorded in Japan: Eutima diademata. A comparative table of the primary diagnostic characteristics of Eutima has been provided. This study provided taxonomic keys for identifying species in the genus Eutima. [ABSTRACT FROM AUTHOR]

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The future food industry is facing urgent challenges that need to be addressed, mainly providing sufficient quantity and quality of food to meet the growing population, reducing environmental pollution, improving production and processing conditions to adapt to climate change, protecting biodiversity, and improving food resource utilization. The global per-capita protein demand and consumption continue to grow, making it difficult for traditional protein supply channels to provide sufficient quantity and quality of protein. Ensuring a high-quality protein supply is an important task for future food processing, and the development trend of future food processing is mainly reflected in sustainable development, health, globalization, energy conservation and emission reduction, nutrition, technology, intelligence, and automation. These factors make the development and application of alternative proteins of great practical significance. Alternative proteins are still in the development stage; the relevant research focuses on the flavor, nutrition, safety, and consumer acceptance of the products. Different drying processes need to adapt to different types and applications of alternative proteins, and the drying process will have a significant impact on the recovery rate, recovery quality, and amino acid composition of proteins. This article provides a brief and comprehensive review of the development background, significance, types, applications, challenges, and solutions of alternative proteins based on the classification criteria of animal and plant derived from the perspective of alternative proteins. Some new drying technologies that can be used for alternative proteins are also introduced, including spray drying (pre-treatment, post-treatment, and optimization of process parameters), vacuum microwave drying, and other combined drying technologies. [ABSTRACT FROM AUTHOR]

One of the main objectives of Palaeolithic art researchers is to study and systematise the form of artistic representations. Some methodologies include the analysis of qualitative variables, linear measurements or the use of geometric morphometry with landmarks. However, these techniques depend to a large extent on the subjectivity of the researcher, which often leads to biased results. To overcome this issue, we present an application of geometric morphometry using Elliptic Fourier Analysis (EFA), together with multivariate statistics and hypothesis testing, for the first time to the study of form in prehistoric art. In order to explain its use, the "duck-bill" convention of pre-Magdalenian horses, often used as a chronological and geographical marker, has been used as a case study. This formalism is described disparately in the literature, so the main objective is to use EFA to determine whether it is possible to characterise this type of convention according to the definitions given by certain authors. The results indicate a possible classification of the heads of these animals. Through this taxonomic proposal, it has been possible to verify the great diversity of forms in which the authors classify the duck-bill horses and, therefore, to demonstrate that this peculiar form can be considered neither a conventionalism nor a chronological/regional marker. In conclusion, the methodology based on EFA combined with multivariate statistics for the objective study of form in prehistoric art is effective and opens a new avenue of analysis in the art of prehistoric societies. [ABSTRACT FROM AUTHOR]

In the last few years, automatic extraction and classification of animal vocalisations has been facilitated by machine learning (ML) and deep learning (DL) methods. Different frameworks allowed researchers to automatically extract features and perform classification tasks, aiding in call identification and species recognition. However, the success of these applications relies on the amount of available data to train these algorithms. The lack of sufficient data can also lead to overfitting and affect generalisation (i.e. poor performance on out-of-sample data). Further, acquiring large data sets is costly and annotating them is time consuming. Thus, how small can a dataset be to still provide useful information by means of ML or DL? Here, we show how convolutional neural network architectures can handle small datasets in a bioacoustic classification task of affective mammalian vocalisations. We explain how these techniques can be used (e.g. pre-training and data augmentation), and emphasise how to implement them in concordance with features of bioacoustic signals. We further discuss whether these networks can generalise the affective quality of vocalisations across different taxa. [ABSTRACT FROM AUTHOR]

The article discusses a conference that took place in Bratislava on May 21-22, 2024, focusing on the intercultural connections between Martin Heidegger's philosophy and Daoism. Scholars explored how Heidegger's engagement with Eastern thought, particularly Daoism, influenced his concept of being. The conference featured presentations on topics such as the freedom of things, the renewal of Chinese philosophy, and the essence of thinking in relation to the Daoist way. The dialogue between Heidegger and Daoism offers insights into contemporary issues that transcend cultural boundaries, such as alienation, technological dependencies, and the search for appropriate approaches to psychological flaws. [Extracted from the article]

Simple Summary: This study addresses the classification task in animal science, which helps organize and analyze complex data, essential for making informed decisions. It introduces Federated Multi-Label Learning (FMLL), a novel approach combining federated learning principles with a multi-label learning technique. Using machine learning strategies, FMLL achieved significant improvements in classification accuracy metrics compared to existing methods. The experimental results on different animal datasets demonstrated the effectiveness of FMLL and its superiority in multi-label classification tasks. The findings of our study offer valuable insights into understanding and managing animal populations, which could have important implications for biodiversity conservation and ecological management. Federated learning is a collaborative machine learning paradigm where multiple parties jointly train a predictive model while keeping their data. On the other hand, multi-label learning deals with classification tasks where instances may simultaneously belong to multiple classes. This study introduces the concept of Federated Multi-Label Learning (FMLL), combining these two important approaches. The proposed approach leverages federated learning principles to address multi-label classification tasks. Specifically, it adopts the Binary Relevance (BR) strategy to handle the multi-label nature of the data and employs the Reduced-Error Pruning Tree (REPTree) as the base classifier. The effectiveness of the FMLL method was demonstrated by experiments carried out on three diverse datasets within the context of animal science: Amphibians, Anuran-Calls-(MFCCs), and HackerEarth-Adopt-A-Buddy. The accuracy rates achieved across these animal datasets were 73.24%, 94.50%, and 86.12%, respectively. Compared to state-of-the-art methods, FMLL exhibited remarkable improvements (above 10%) in average accuracy, precision, recall, and F-score metrics. [ABSTRACT FROM AUTHOR]

The process of learning theory and the limited ability to remember anything, especially a foreign language, often cause students to have difficulty understanding lessons, especially in determining the type and taxonomy of the animal. With the assistance of computer vision technology, students can more effectively face various challenges, enhance their understanding, and improve their ability to apply the concept of animal classification. The research classifies the taxonomy of 90 animals using Transfer Learning ResNet 152. It aims to analyze the performance of Transfer Learning ResNet 152 on the 90-animal dataset. The results show that in Model A with an architecture with frozen layers in 6 ResNet blocks, the highest evaluation value obtained is 0.9222 on Batch size 4 with Dropout 6, 0.9241 on Batch size 8 with Dropout 7, 0.9259 on Batch size 16 with Dropout 8, and 0.9296 on Batch size 32 with Dropout 4 and Dropout 7. Meanwhile, in model B with an architecture with frozen layers in 5 ResNet blocks and one non-frozen block, the highest evaluation value obtained is 0.7611 on Batch size 4 with Dropout 8, 0.8713 on Batch size 8 with Dropout 2, 0.8852 on Batch size 16 with Dropout 1, and 0.9204 on Batch size 32 with Dropout 3. [ABSTRACT FROM AUTHOR]

Metazoa‐level universal single‐copy orthologs (mzl‐USCOs) are universally applicable markers for DNA taxonomy in animals that can replace or supplement single‐gene barcodes. Previously, mzl‐USCOs from target enrichment data were shown to reliably distinguish species. Here, we tested whether USCOs are an evenly distributed, representative sample of a given metazoan genome and therefore able to cope with past hybridization events and incomplete lineage sorting. This is relevant for coalescent‐based species delimitation approaches, which critically depend on the assumption that the investigated loci do not exhibit autocorrelation due to physical linkage. Based on 239 chromosome‐level assembled genomes, we confirmed that mzl‐USCOs are genetically unlinked for practical purposes and a representative sample of a genome in terms of reciprocal distances between USCOs on a chromosome and of distribution across chromosomes. We tested the suitability of mzl‐USCOs extracted from genomes for species delimitation and phylogeny in four case studies: Anopheles mosquitos, Drosophila fruit flies, Heliconius butterflies and Darwin's finches. In almost all instances, USCOs allowed delineating species and yielded phylogenies that corresponded to those generated from whole genome data. Our phylogenetic analyses demonstrate that USCOs may complement single‐gene DNA barcodes and provide more accurate taxonomic inferences. Combining USCOs from sources that used different versions of ortholog reference libraries to infer marker orthology may be challenging and, at times, impact taxonomic conclusions. However, we expect this problem to become less severe as the rapidly growing number of reference genomes provides a better representation of the number and diversity of organismal lineages. [ABSTRACT FROM AUTHOR]

Machine learning‐based behaviour classification using acceleration data is a powerful tool in bio‐logging research. Deep learning architectures such as convolutional neural networks (CNN), long short‐term memory (LSTM) and self‐attention mechanism as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration‐based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached and complexity in data due to complex animal‐specific behaviours, which may have limited the application of deep learning techniques in this area.To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup and pre‐training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state‐of‐the‐art deep learning model architectures.Data augmentation improved the overall model performance when one of the various techniques (none, scaling, jittering, permutation, time‐warping and rotation) was randomly applied to each data during mini‐batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre‐training with unlabelled data did not improve model performance. The state‐of‐the‐art deep learning models, including a model consisting of four CNN layers, an LSTM layer and a multi‐head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features.Our experiments showed that deep learning techniques are promising for acceleration‐based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning and self‐supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time‐series sensor data. [ABSTRACT FROM AUTHOR]

Background: Longevity and resilience are two fundamental traits for more sustainable livestock production. These traits are closely related, as resilient animals tend to have longer lifespans. An interesting criterion for increasing longevity in rabbit could be based on the information provided by its gut microbiome. The gut microbiome is essential for regulating health and plays crucial roles in the development of the immune system. The aim of this research was to investigate if animals with different longevities have different microbial profiles. We sequenced the 16S rRNA gene from soft faeces from 95 does. First, we compared two maternal rabbit lines with different longevities; a standard longevity maternal line (A) and a maternal line (LP) that was founded based on longevity criteria: females with a minimum of 25 parities with an average prolificacy per parity of 9 or more. Second, we compared the gut microbiota of two groups of animals from line LP with different longevities: females that died/were culled with two parities or less (LLP) and females with more than 15 parities (HLP). Results: Differences in alpha and beta diversity were observed between lines A and LP, and a partial least square discriminant analysis (PLS-DA) showed a high prediction accuracy (> 91%) of classification of animals to line A versus LP (146 amplicon sequence variants (ASV)). The PLS-DA also showed a high prediction accuracy (> 94%) to classify animals to the LLP and HLP groups (53 ASV). Interestingly, some of the most important taxa identified in the PLS-DA were common to both comparisons (Akkermansia, Christensenellaceae R-7, UnculturedEubacteriaceae, among others) and have been reported to be related to resilience and longevity. Conclusions: Our results indicate that the first parity gut microbiome profile differs between the two rabbit maternal lines (A and LP) and, to a lesser extent, between animals of line LP with different longevities (LLP and HLP). Several genera were able to discriminate animals from the two lines and animals with different longevities, which shows that the gut microbiome could be used as a predictive factor for longevity, or as a selection criterion for these traits. [ABSTRACT FROM AUTHOR]

The article discusses various scientific studies published in the Proceedings of the National Academy of Sciences of the United States of America. The studies cover a range of topics, including preserved magnetism in meteorites, group formation among solitary animals, expansion and recession of human populations, mismatch between plant green-up and bird migration, genomics of Tasmanian devil cancer, elderly populations and energy consumption, and fluctuations in the Patagonian ice sheet. The studies provide valuable insights into these subjects and contribute to our understanding of the natural world. [Extracted from the article]

The anatomy of North American tortoises is poorly understood, despite a rich fossil record from the Eocene and younger strata. Stylemys nebrascensis is a particularly noteworthy turtle in this regard, as hundreds of specimens are known from Oligocene deposits, and as this species is one of the earliest fossil turtles to have been described in the scientific literature. Since its initial description based on a shell, many specimens with more complete material have been referred to Stylemys nebrascensis. Here, we review and confirm the referral of an important historic specimen to Stylemys nebrascensis, which includes shell, non-shell postcranial, and skull material. This allows us to document unique skull features of Stylemys nebrascensis (e.g., an unusual 'poststapedial canal' that connects the posterior skull surface with the cavum acustico-jugulare) and to refer another well-preserved skull to the species. Based on computed-tomography scanning of these two skulls, we provide a detailed description of the cranial and mandibular osteology of Stylemys nebrascensis. Stylemys nebrascensis has a combination of plesiomorphic skull characteristics (e.g., retention of a medial jugal process) and derived traits shared with extant gopher tortoises (e.g., median premaxillary ridge) that suggest it may be a stem-representative of the gopher tortoise lineage. This supports the hypothesis that extant and fossil tortoises from North America form a geographically restricted clade that split from Asian relatives during the Paleogene. [ABSTRACT FROM AUTHOR]

Prior aquatic animal image classification research focused on distinguishing external features in controlled settings, utilizing either digital cameras or webcams. Identifying visually similar species, like Short mackerel (Rastrelliger brachysoma) and Indian mackerel (Rastrelliger kanagurta), is challenging without specialized knowledge. However, advancements in computer technology have paved the way for leveraging machine learning and deep learning systems to address such challenges. In this study, transfer learning techniques were employed, utilizing established pre-trained models such as ResNet50, Xception, InceptionV3, VGG19, VGG16, and MobileNetV3Small. These models were applied to differentiate between the two species using raw images captured by a smartphone under uncontrolled conditions. The core architecture of the pre-trained models remained unchanged, except for the removal of the final fully connected layer. Instead, a global average pooling layer and two dense layers were appended at the end, comprising 1024 units and by a single unit, respectively. To mitigate overfitting concerns, early stopping was implemented. The results revealed that, among the models assessed, the Xception model exhibited the most promising predictive performance. It achieved the highest average accuracy levels of 0.849 and 0.754 during training and validation, surpassing the other models. Furthermore, fine-tuning the Xception model by extending the number of epochs yielded more impressive outcomes. After 30 epochs of fine-tuning, the Xception model demonstrated optimal performance, reaching an accuracy of 0.843 and displaying a 11.508% improvement in predictions compared to the model without fine-tuning. These findings highlight the efficacy of transfer learning, particularly with the Xception model, in accurately distinguishing visually similar aquatic species using smartphone-captured images, even in uncontrolled conditions. [ABSTRACT FROM AUTHOR]

This study examines the usage of the verb "barxīzīdan" in ancient Persian texts with a specific focus on its application in referring to "havāmm" and "xalq al-sā'ah". These texts, which incorporate elements of zoology, commonly employ Arabic terms to categorize animals based on their habitat and mode of movement. Furthermore, they contain references to a distinct group of animals believed to have been created according to the naturalists' beliefs of ancient times, known as "xalq al-sā'ah". This research aims to offer a thorough and analytical exploration of how translators and writers who have demonstrated an interest in the Persian language have utilized "barxīzīdan" from two semantic domains to denote these two animal groups. By carefully examining the zoological content of these texts and combining specialized knowledge in zoology with linguistic expertise, it becomes possible to discern subtle nuances in meaning between the terms "xīzandigān" and "barxīzandigān". This study primarily focuses on Tuhfat al-Ghrāib by Hāsib Tabarī, a scholar of the 5th and 6th (AH) centuries, which stands as the earliest known Persian encyclopedic text of its kind. Additionally, other texts, including a translation of Nahj al-Balāgha, likely from the same time, and Nuzhat Nāmah by Shahmardān Rāzī, are examined from this perspective, along with other zoological encyclopedia texts, to validate this classification criterion within ancient texts. Our findings suggest that Hāsib Tabarī has attributed a novel significance to the term "barxīzandigān" ("xalq al-sā'ah" creatures) within the context of his work, a meaning not previously documented in other dictionaries. [ABSTRACT FROM AUTHOR]

This study examines the usage of the verb "barxīzīdan" in ancient Persian texts with a specific focus on its application in referring to "havāmm" and "xalq al-sā'ah". These texts, which incorporate elements of zoology, commonly employ Arabic terms to categorize animals based on their habitat and mode of movement. Furthermore, they contain references to a distinct group of animals believed to have been created according to the naturalists' beliefs of ancient times, known as "xalq al-sā'ah". This research aims to offer a thorough and analytical exploration of how translators and writers who have demonstrated an interest in the Persian language have utilized "barxīzīdan" from two semantic domains to denote these two animal groups. By carefully examining the zoological content of these texts and combining specialized knowledge in zoology with linguistic expertise, it becomes possible to discern subtle nuances in meaning between the terms "xīzandigān" and "barxīzandigān". This study primarily focuses on Tuhfat al-Ghrāib by Hāsib Tabarī, a scholar of the 5th and 6th (AH) centuries, which stands as the earliest known Persian encyclopedic text of its kind. Additionally, other texts, including a translation of Nahj al-Balāgha, likely from the same time, and Nuzhat Nāmah by Shahmardān Rāzī, are examined from this perspective, along with other zoological encyclopedia texts, to validate this classification criterion within ancient texts. Our findings suggest that Hāsib Tabarī has attributed a novel significance to the term "barxīzandigān" ("xalq al-sā'ah" creatures) within the context of his work, a meaning not previously documented in other dictionaries.

Simple Summary: Providing better living conditions for laying hens by promoting free-range systems has sparked curiosity about potential alterations in their dietary habits. Understanding these changes can lead to new feeding strategies to meet their nutritional needs. However, this task is challenging due to the inherent complexities of the outdoor environments and variations in plant material breakdown. Our research marks a pioneering step in exploring both qualitative and quantitative methods for assessing plant intake in laying hens using n-alkanes as markers, and demonstrates their viability even at levels of plant material in the diet as low as 1%. It also highlights the need for further research on different sources of n-alkanes, especially in real case studies with different plant and insect species under outdoor conditions. Such studies are essential to provide a deeper understanding of the dietary factors affecting the performance of laying hens in free-range systems. The shift in animal welfare standards towards free-range housing for laying hens in the EU has raised questions about changes in dietary composition. Accurate assessment of outdoor plant material intake is crucial for effective feeding strategies. This study introduces an approach using n-alkanes as markers to determine plant intake in laying hens, involving n-alkane recovery rate assessment, discriminant analysis and linear equation-solving for both qualitative and quantitative assessment, respectively, considering systematic n-alkane combinations. Two diets: a standard commercial diet and a diet incorporating 1% alfalfa were tested. Chemical analyses showed an altered n-alkane profile due to alfalfa inclusion, resulting a recovery rates ranging from 30–44% depending on the n-alkane type and diet. Statistical analysis revealed significant differences in recovery rates among the different alkanes for the same diets and between the diets for the same alkane, together with an interaction between n-alkane carbon chain length and initial concentration in the diet. The method accurately predicted plant inclusion, with a slight overestimation (2.80%) using the combination C25-C29-C33. Accurate qualitative classification of the animals based on fecal n-alkanes profiles was observed. The study successfully demonstrated the utility of n-alkanes for estimating dietary composition, providing a non-invasive approach for future free-range studies. [ABSTRACT FROM AUTHOR]

Deep learning models that require vast amounts of training data struggle to achieve good animal sound classification (ASC) performance. Among recent few-shot ASC methods to address the data shortage problem regarding animals that are difficult to observe, model-agnostic meta-learning (MAML) has shown new possibilities by encoding common prior knowledge derived from different tasks into the model parameter initialization of target tasks. However, when the knowledge on animal sounds is difficult to generalize due to its diversity, MAML exhibits poor ASC performance due to the static initialization setting. In this paper, we propose a novel task-adaptive parameter transformation scheme called few-shot ASC. TAPT generates transformation variables while learning common knowledge and uses the variables to make parameters specific to the target task. Owing to this transformation, TAPT can reduce overfitting and enhance adaptability, training speed, and performance in heterogeneous tasks compared to MAML. In experiments on two public datasets on the same backbone network, we show that TAPT outperforms the existing few-shot ASC schemes in terms of classification accuracy, and in particular a performance improvement of 20.32% compared to the state-of-the-art scheme. In addition, we show that TAPT is robust to hyperparameters and efficient for training. [ABSTRACT FROM AUTHOR]

Wildlife roadkill is a recurring, dangerous problem that affects both humans and animals and has received increasing attention from environmentalists worldwide. Addressing this problem is difficult due to the high investments required in road infrastructure to effectively reduce wildlife vehicle collisions. Despite recent applications of machine learning techniques in low-cost and economically viable detection systems, e.g., for alerting drivers about the presence of animals and collecting statistics on endangered animal species, the success and wide adoption of these systems depend heavily on the availability of data for system training. The lack of training data negatively impacts the feature extraction of machine learning models, which is crucial for successful animal detection and classification. In this paper, we evaluate the performance of several state-of-the-art object detection models on limited data for model training. The selected models are based on the YOLO architecture, which is well-suited for and commonly used in real-time object detection. These include the YoloV4, Scaled-YoloV4, YoloV5, YoloR, YoloX, and YoloV7 models. We focus on Brazilian endangered animal species and use the BRA-Dataset for model training. We also assess the effectiveness of data augmentation and transfer learning techniques in our evaluation. The models are compared using summary metrics such as precision, recall, mAP, and FPS and are qualitatively analyzed considering classic computer vision problems. The results show that the architecture with the best results against false negatives is Scaled-YoloV4, while the best FPS detection score is the nano version of YoloV5. [ABSTRACT FROM AUTHOR]

Timely and accurate detection and estimation of animal abundance is an important part of wildlife management. This is particularly true for invasive species where cost-effective tools are needed to enable landscape-scale surveillance and management responses, especially when targeting low-density populations residing in dense vegetation and under canopies. This research focused on investigating the feasibility and practicality of using uncrewed aerial systems (UAS) and hyperspectral imagery (HSI) to classify animals in the wild on a spectral—rather than spatial—basis, in the hopes of developing methods to accurately classify animal targets even when their form may be significantly obscured. We collected HSI of four species of large mammals reported as invasive species on islands: cow (Bos taurus), horse (Equus caballus), deer (Odocoileus virginianus), and goat (Capra hircus) from a small UAS. Our objectives of this study were to (a) create a hyperspectral library of the four mammal species, (b) study the efficacy of HSI for animal classification by only using the spectral information via statistical separation, (c) study the efficacy of sequential and deep learning neural networks to classify the HSI pixels, (d) simulate five-band multispectral data from HSI and study its effectiveness for automated supervised classification, and (e) assess the ability of using HSI for invasive wildlife detection. Image classification models using sequential neural networks and one-dimensional convolutional neural networks were developed and tested. The results showed that the information from HSI derived using dimensionality reduction techniques were sufficient to classify the four species with class F1 scores all above 0.85. The performances of some classifiers were capable of reaching an overall accuracy over 98%and class F1 scores above 0.75, thus using only spectra to classify animals to species from existing sensors is feasible. This study discovered various challenges associated with the use of HSI for animal detection, particularly intra-class and seasonal variations in spectral reflectance and the practicalities of collecting and analyzing HSI data over large meaningful areas within an operational context. To make the use of spectral data a practical tool for wildlife and invasive animal management, further research into spectral profiles under a variety of real-world conditions, optimization of sensor spectra selection, and the development of on-board real-time analytics are needed. [ABSTRACT FROM AUTHOR]

This text explores the concept of terminology and its connection to specialized fields. It discusses the importance of understanding the implicit relationships between terms and the development of systems like taxonomies and nomenclature. The text also delves into the use of ontologies and frames in terminology work, highlighting the benefits of classifying relations and treating them as values. It compares two approaches to ontologies and includes articles and case studies that provide different perspectives on the topic. These case studies examine the advantages of ontologies and frames in domains such as translation, legal terminology, and gastronomy. [Extracted from the article]

'Mass extinctions' have been a hot topic for several decades. What triggers a mass extinction? How does a mass extinction impact the evolution of life? How does our ecosystem recover after a mass extinction? These questions attracted the interest of both scientists and the public alike. NSR spoke to two renowned researchers in the field of mass extinctions: Prof. Douglas H. Erwin from the National Museum of Natural History of the USA, and Prof. Shuzhong Shen from Nanjing University, China. Prof. Erwin has been interested in the end-Permian mass extinction since graduate school. He has worked on a variety of problems, from Permian gastropod systematics to the origin of animals. Currently his work focuses on the nature of evolutionary novelty and innovation. Prof. Shen's research career is centered upon the end-Permian mass extinction, Permian stratigraphy and global correlations, with taxonomic expertise on brachiopods and conodonts. The International Commission on Stratigraphy recognizes his outstanding singular contribution to stratigraphy and awarded him the ICS Stratigraphy Medal in 2019. [ABSTRACT FROM AUTHOR]

This article, published in the journal Crustaceana, discusses the first recorded sighting of Caromiobenella helgolandica in the Okhotsk Sea. Monstrilloid copepods are parasites of benthic invertebrates and have a planktonic adult stage. The genus Caromiobenella contains about 10 species, including C. helgolandica, which has a worldwide distribution but had not been previously recorded in Russia or adjacent waters. The article provides a detailed description of the female specimen collected from the Okhotsk Sea, including its morphology and distinctive characteristics. [Extracted from the article]

Dr. Donald G. Buth, a respected professor at UCLA, passed away on May 31, 2022. He had a distinguished academic career, teaching numerous courses in biology and receiving several teaching awards. Dr. Buth was also an accomplished researcher, focusing on phylogenetic systematics, fish parasites, and the genetics of ecosystems. He served as an editor for the Journal of Ichthyology and Herpetology and was involved in various scientific organizations. Dr. Buth's impact on his students and the field of ichthyology is evident through the species named in his honor. [Extracted from the article]

There are many kinds of Marine organisms and their biological forms differ greatly, so it is difficult to guarantee the accuracy of artificial species identification, which brings great challenges to the work of Marine species identification. In this paper, we propose a recognition method of Marine biological image classification using residual neural network, redefining convolution layer and using batch regularization to avoid gradient parameter disorder. The bottleneck layer is realized by the residual connection in the neural network, and the residual network ResNet50 is constructed by the transfer learning method. The classification training was conducted on 19 common Marine animal data sets, and the experimental results showed that the recognition accuracy of ResNet50 reached about 90%. Compared with the traditional convolutional neural network VGG19, the results showed that the recognition efficiency of ResNet50 was better, thus verifying the effectiveness of the Marine animal classification and recognition model proposed in this paper. [ABSTRACT FROM AUTHOR]

The bilateral asymmetry of the morphotypes m1 of the root vole Alexandromys oeconomus Pallas, 1776 from different parts of the range has been analyzed from morphogenetic positions. The material from the collection of the Institute of Systematics and Ecology of Animals, Siberian Branch, Russian Academy of Sciences (Novosibirsk), is used. A total of 2314 pairs of morphotypes m1 have been studied on the left and right rami of the mandible of voles. Seventeen different morphotypes and 70 different combinations of them are identified, of which 13 are symmetrical and 57 are asymmetrical. The data of bilateral asymmetry of morphotypes for each sample is presented in the form of square contingency tables. Based on these tables, the information-entropy parameters of the samples are calculated, in accordance with which the eco-geographic factors of the habitat are put. The 2B–PLS method is applied to the matrices after standardization. Two significant directions of contingency variability of the information-entropy parameters of the samples and eco-geographic factors of the environment have been identified. The first direction, latitudinal, is associated with a decrease in the Shannon entropy and fluctuating asymmetry in the northern samples of the root vole. Thus, the northern samples have a higher degree of morphogenetic stability compared to the southern ones. The second—the longitude direction—is highly correlated with the increase in altitude above sea level from west to east and is accompanied by a decrease in the values of the Cohen coefficient, Shannon information, and an increase in fluctuating asymmetry. This indicates a lower degree of morphogenetic stability of the eastern samples compared to the western ones. The approach we use complements the classical concept of the "epigenetic landscape" with the ability to take into account the influence of the environment. This is important in the practical use of asymmetry indicators for assessing the "health of the environment," especially when determining the natural background for populations of the "ecological periphery" of the areal. [ABSTRACT FROM AUTHOR]

The subject of this article is a set of legal acts in Ukraine that regulate the general procedure and principles of animal treatment and the forms/methods of ensuring their welfare in the modern world. For a long time, the issues of protection and maintenance of normal conditions for the existence of the animal world have been somewhere “alongside” the need to regulate social relations, although everyone knows that the model of animal treatment is a prototype of a human behavioural algorithm in society. The purpose of the article is to provide a general analysis of the essence and content of the regulatory provisions governing the process and procedure of animal treatment, with a view to further clarifying the forms and means of preventing such treatment. Achieving this goal requires solving a number of tasks, including: providing a general description of the regulatory framework for the existence of animals in modern Ukraine; developing a specific direction for improving the national legislation of Ukraine, the purpose of which should be to more effectively regulate human-animal relations in all areas, with the maximum possible exclusion of cruelty to animals. The article presents arguments and draws conclusions on improving the classification of animals in national legislation and systematising regulations in this area, which should ultimately ensure normal conditions for the existence of animals in Ukraine. The issue of defining the concept of fauna in modern Ukraine is rather limited and does not correspond to the concept of modern interaction between humans and animals in many areas of public life. In a state governed by the rule of law, all spheres of public life must be effectively regulated, which explains the need to consolidate all existing legal acts in Ukraine in the field of animal welfare and protection and bring them into line with international standards. This issue is extremely important precisely because humanity cannot exist without the animal world. Such improvements are aimed at clearly defining procedural algorithms for detecting, recording, preventing and suppressing cruelty to animals.

With the development of sensor technologies, sensors have become increasingly embedded in various fields, becoming an indispensable part of our daily lives, research, and work. Notably, in ethology, surveillance cameras, a type of optical sensor, are extensively used alongside machine learning to analyze animal behaviors. However, simply feeding vast amounts of sensor data into servers for processing is neither efficient nor sustainable. In line with the prevailing trend towards edge computing, it is becoming increasingly important to process and integrate the captured sensor information directly within the sensor itself. While we have not fully achieved this, the application of deep learning methods to facilitate efficient and rapid processing with low computational demands is a necessary progression. In our study, we used a method for outdoor animal behavior analysis using multi-target classification, taking advantage of the potential efficiency gains provided by deep learning. We focus on a polar bear's behaviors captured by an IoT-enabled surveillance camera in a zoo. The image data are first analyzed by using an object detection model to provide location sequences, movement speed, and coordinates of video frames, representing the animal's state. Using these sensor data, we developed a classification model that accurately classifies multiple behaviors. The detection of these behaviors, including stereotypical behavior, illustrates the potential of our system to comprehensively monitor the animal health status. Our method achieved accurate detection [98.3% average precision (AP) 50] and multi-behavior recognition (accuracy of 89.5%), while maintaining robustness against outdoor noise. [ABSTRACT FROM AUTHOR]

This paper explores what happens to care, and decisions about ending and extending life, when research animals become pets and pets become research animals. To do this, we draw on in-depth qualitative research on (i) rehoming of laboratory animals, (ii) veterinary clinical research, and (iii) the role of the Named Veterinary Surgeon (NVS) in UK animal research. We begin by exploring how (in theory and practice) the ethical, affective, and practical elements of care are split in the research laboratory. We then investigate arguments for and against ending and extending animal life via clinical research and rehoming, highlighting how these activities bring norms and dilemmas around animal death in the laboratory and veterinary clinic to the fore. We conclude by demonstrating the value of investigating borders between animal categories for understanding dilemmas around care and death, and for contributing to emerging literatures within geography around animal care, death, and categorisation. Key contributions of our work include highlighting: how care roles can be split; the importance of considering speculative and in-practice elements of care; the context-dependency and multiplicity of practices of killing in the veterinary clinic and laboratory; and the flexibility and changing nature of animal categories. [ABSTRACT FROM AUTHOR]

Biological nomenclature harks back to a remote prehistoric past, as shown by the universality of fairly sophisticated folk taxonomies and nomenclatures found on all inhabited continents. Ethnobiologists have suggested that these nomenclatures include cryptic 'ethnotaxonomic ranks', although the existence of these ranks has been increasingly questioned recently. The fact that no trace of such ranks has been evoked in Aristotle's classification of animals but that they have been described in antique Roman ethnotaxonomies casts further doubts about these cryptic ranks. The advent of rank‐based nomenclature (RN) in the mid‐18th century has had a pervasive, but not only positive, influence on biological nomenclature. The use of a single type and of a subjective, artificial nomenclatural rank does not delimit taxa under RN. This is even a goal of RN, according to Principle 2 of the Zoological Code. This contrasts with the nomenclatures of other fields, some of which are designed to delimit entities fairly precisely (e.g. geopolitics, stratigraphy, chemistry), and in which ranks are either defined more objectively (e.g. geopolitics, chemistry), or used informally and relegated to a secondary role (e.g. biogeography, paleobiogeography), or vary in time (e.g., paleobiogeography) or space (e.g., stratigraphy). A trend towards more explicit and precise delimitation of entities over time is also discernible in some fields, especially geopolitics and stratigraphy. In this context, the development of phylogenetic nomenclature (PN) and the recent advent of the PhyloCode appear as the logical outcome of the development of evolutionary biology and phylogenetics. [ABSTRACT FROM AUTHOR]

Since the late 2010s, Artificial Intelligence (AI) including machine learning, boosted through deep learning, has boomed as a vital tool to leverage computer vision, natural language processing and speech recognition in revolutionizing zoological research. This review provides an overview of the primary tasks, core models, datasets, and applications of AI in zoological research, including animal classification, resource conservation, behavior, development, genetics and evolution, breeding and health, disease models, and paleontology. Additionally, we explore the challenges and future directions of integrating AI into this field. Based on numerous case studies, this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom. As we build a bridge between beast and byte realms, this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored. [ABSTRACT FROM AUTHOR]

The continuous monitoring of animals is crucial for the well-being of both humans and animals. A comprehensive animal monitoring system must incorporate animal detection, classification, and deterrence techniques. This review paper addresses 8 research questions related to animal monitoring by presenting a comprehensive literature review of animal deterrence, monitoring, classification, and detection techniques. Additionally, it covers various animal image acquisition techniques, different image modalities, photogrammetry types, and unmanned vehicles used for animal studies. The paper also highlights the problems faced by animals and humans in co-existence and lists the challenges faced while capturing animal images in different modalities, such as visible, thermal, and aerial images. The conclusion includes a comparative study based on benchmark datasets and highlights future scope and areas that require further research in animal monitoring systems. [ABSTRACT FROM AUTHOR]

The article seeks to develop an alternative model of biopolitics based on the experience of stray dogs in the context of the transformation of the modern urban fabric. Its main goal is to show, on the one hand, that the purificatory actions of biopower in the context of street-dwelling dogs were an inalienable condition of modern urbanization in the West Atlantic variant, which made one of its pillars the multi-scalar process of spatial segregation of nonhuman animals. On the other, to offer such a look at strayness that, by violating the modern zoological machine and disarming the urban fear of animal collectivity, would open urban politics and urbanization to new possibilities emerging from the experience of the multispecies crowd. Another important task of the article will be to rethink the ontological status of stray dogs, which elude most, essentially biopolitical classifications of urban animals, revealing the political potential of the category of ferality. The article consists of five parts. In the first, I refer to existing accounts of animal biopolitics, noting their merits and the main limitation, namely, the lack of in-depth reflection on such a politics of life that would take into account the autonomy and agency of animal subjects. In the second, I develop the notion of the zoological machine (with reference to Agamben's philosophy), having in mind the semiotic-material apparatuses responsible for the production and maintenance of species differences, as well as the concealment of related violence. The third part serves to show its workings through the example of stray dogs in Victorian cities. My focus here - following Chris Pearson - is to show the interdependent processes of canine killing and the emergence of dogopolis (i.e., new, typically Western dog-human relationships), which turn out to be a key component of modern urbanization. In part four, I analyze the potentials of "ferality", seeing it as a field of opportunity for alternative interspecies practices that hold the promise of disarming the Western zoological machine. The fifth part and the conclusion serve to present two key cases (Moscow and Chernobyl), against the background of which I develop the aforementioned biopolitical alternative. In doing so, I use such concepts as commoning, inhabitation, and the figure of the neighbor. [ABSTRACT FROM AUTHOR]

Enteric methane (CH4) is produced during the anaerobic fermentation of organic matter and represents an energy loss for ruminants. Diet and animal characteristics mainly determine CH4 emissions. However, there is limited information regarding the relationship between feeding behavior and CH4 emissions in growing steers consuming different diets. This experiment aimed to evaluate the feeding behavior of growing steers with divergent residual methane emissions (RCH4) during the backgrounding and finishing phases. Two hypotheses were evaluated: 1) the steers would not change the initial RCH4 classification when moving from backgrounding to finishing phases, and 2) steers with divergent RCH4 would show different feeding behavior. Angus steers (n = 60) were placed in two pens and were classified as high, medium, and low RCH4 during both phases. During the backgrounding phase, steers [209 ± 26 kg of initial body weight (BW)] were fed a total mixed ratio (TMR) with 46% neutral detergent fiber (NDF) and 0.86 Mcal/kg DM of net energy of gain (NEg) for 60 d. During the finishing phase, steers (517 ± 40 kg of initial BW) were fed a TMR with 16.8% NDF and 1.37 Mcal/kg DM of NEg for 80 d. Feed intake and gas flux were determined daily using Smartfeed and Greenfeed technology. Steers were weighed every 2 wk during each phase to determine average daily gain (ADG). The number of meals (NM), meal size (MS), time per meal (TM), intervals between meals (IM), and intake rate (IR) were calculated using 5 min as a meal criterion. Individual RCH4 classification rank during the backgrounding and finishing phases was analyzed using a chi-square test. Feeding behavior was analyzed using a mixed model with RCH4 classification and animal as fixed and random effects, respectively. No relation (P = 0.5) existed between individual RCH4 classification during the backgrounding and the subsequent RCH4 classification during the finishing phase. In either phase, feed intake or ADG was not different (P > 0.05) among divergent RCH4 groups. Feeding behavior was not different (P > 0.05) among RCH4 groups during the backgrounding phase, but MS and TM were greater (P < 0.05) in steers with high RCH4 during the finishing phase. Steers classified as low RCH4 showed less CO2 and CH4 production and less O2 consumption than medium and high RCH4 in either phase. In conclusion, growing steers were re-ranked according to the RCH4 classification during the backgrounding and finishing phases. Animal performance was not related to RCH4 classification. However, steers with decreased RCH4 showed less MS during the finishing phase. Also, CO2 and CH4 were less in low RCH4 steers during both phases. Further work should be conducted to recognize differences in fermentation and metabolism of lower RCH4 steers for developing selection and management interventions. [ABSTRACT FROM AUTHOR]

Janine Ziermann-Canabarro is an established researcher in the field of biology, specifically in the areas of development and evolution, head muscles, and the heart. She has received prestigious awards for her papers on cranial muscle development and muscle identity in evolutionary morphology. Ziermann-Canabarro studied biology at Friedrich Schiller University in Jena, Germany, under the guidance of Prof. Dr. Lennart Olsson. Her interest in biology stemmed from her love for animals and the interconnectedness of life through evolution. She credits Dr. Julia C. Boughner as her most influential mentor, who has guided her in research, leadership, and outreach. Ziermann-Canabarro's favorite paper is on avian craniofacial muscles, which inspired her to pursue cranial muscle research. She believes that the future of evolutionary developmental biology (EvoDevo) lies in studying zoonoses and their impact on development and evolution, as well as utilizing new methodologies and datasets. Her advice to junior EvoDevo researchers is to not hesitate in asking questions and seeking opportunities. [Extracted from the article]

Simple Summary: According to European regulations, the severity of the suffering of animals during animal experiments should be assessed. Regulatory documents and guidelines provide recommendations on how to approach this severity assessment; however, they are often not specific enough, resulting in inconsistencies between different institutes performing the same procedures. To overcome this, two Belgian academic institutions with a focus on biomedical research, collaborated to develop and align the severity classification for all procedures performed. This was performed based on the available literature and guidelines, as well as the professional judgment of the designated veterinarians, animal welfare bodies and animal ethics committees. Throughout the manuscript, we motivate which criteria were used to classify procedures or groups of procedures within a specific category. Our collaborative classification includes many procedures and disease models in a variety of animal species for which a severity classification was not reported so far, or the terms that assign them to a different severity were too vague. We believe this extensive list of procedures and the approach described in this paper could be of great value to other research institutions. According to the EU Directive 2010/63, all animal procedures must be classified as non-recovery, mild, moderate or severe. Several examples are included in the Directive to help in severity classification. Since the implementation of the Directive, different publications and guidelines have been disseminated on the topic. However, due to the large variety of disease models and animal procedures carried out in many different animal species, guidance on the severity classification of specific procedures or models is often lacking or not specific enough. The latter is especially the case in disease models where the level of pain, suffering, distress and lasting harm depends on the duration of the study (for progressive disease models) or the dosage given (for infectious or chemically induced disease models). This, in turn, may lead to inconsistencies in severity classification between countries, within countries and even within institutions. To overcome this, two Belgian academic institutions with a focus on biomedical research collaborated to develop a severity classification for all the procedures performed. This work started with listing all in-house procedures and assigning them to 16 (sub)categories. First, we determined which parameters, such as clinical signs, dosage or duration, were crucial for severity classification within a specific (sub)category. Next, a severity classification was assigned to the different procedures, which was based on professional judgment by the designated veterinarians, members of the animal welfare body (AWB) and institutional animal ethics committee (AEC), integrating the available literature and guidelines. During the classification process, the use of vague terminology, such as 'minor impact', was avoided as much as possible. Instead, well-defined cut-offs between severity levels were used. Furthermore, we sought to define common denominators to group procedures and to be able to classify new procedures more easily. Although the primary aim is to address prospective severity, this can also be used to assess actual severity. In summary, we developed a severity classification for all procedures performed in two academic, biomedical institutions. These include many procedures and disease models in a variety of animal species for which a severity classification was not reported so far, or the terms that assign them to a different severity were too vague. [ABSTRACT FROM AUTHOR]

The pelvis, formed by the ilium, ischium and pubis, forms the coxal, which is the largest of the flat bones of the skeleton, with an important role in the physiology of birth. Vastly studied in domestic animals, there is still much to be investigated regarding the anatomical classification in wild animals, mainly aiming to improve the understanding of the reproductive management of the species. Tamandua tetradactyla is one of these species in which morphological studies are still scarce, especially when it comes to reproductive aspects. In this context, we carried out the pelvimetry of T. tetradactyla using radiographic images to anatomically and obstetrically classify the pelvis of this species, classifying it as dolicopelvic, with evidence of homoscedasticity of the samples. Verification of the existence of a relationship between the variables studied by the statistical method of Pearson coefficients showed positive correlations of high intensity for the diameters studied (P < 0.01). Thus, we believed that our findings may support future reproductive studies in this species. [ABSTRACT FROM AUTHOR]

The rapid decline in wildlife animal diversity necessitates expedited evaluations of biodiversity and population dynamics. Accurate image recognition from camera traps is central to such assessments. This study investigates the impact of different optimisation techniques and hyperparameter configurations on the accuracy of wildlife animal classification. Specifically, the comparative effectiveness of the Adaptive Moment Estimation (Adam) and Root Mean Square Propagation (RMSProp) optimisation algorithms is examined. The influence of learning rates on these optimisation techniques is evaluated, while other hyperparameters are held constant. Convolutional Neural Networks (CNN) models, namely DenseNet-121, ResNet-50, and AlexNet, are utilised for this study. The investigation employs a dataset composed of 47,841 images sourced from the Serengeti Project Season 1 Snapshot in Tanzania. The images depict wild animals in diverse perspectives within their natural habitats, with some providing a complete view of the animal's body, while others do not. The dataset, characterised by an imbalanced distribution, is segregated into training, validation, and testing sets at proportions of 80%, 10%, and 10%, respectively. The results reveal that the application of the Adam optimisation technique yields the highest average accuracy of 80.66% with the ResNet-50 model. However, the DenseNet-121 model achieved an overall accuracy exceeding 95%. Notably, the ResNet-50 architecture, with learning rates of 0.1 and 0.01, encountered challenges during the training and validation of all images due to the complexity of the dataset. Irrespective of the optimisation technique employed, the most effective performance was observed with the ResNet-50 model, utilising the Adam optimiser and a learning rate of 0.001. The study proposes suitable learning rate values for training scenarios similar to the present investigation. [ABSTRACT FROM AUTHOR]

Background: Biologgers have contributed greatly to studies of animal movement, behaviours and physiology. Accelerometers, among the various on-board sensors of biologgers, have mainly been used for animal behaviour classification and energy expenditure estimation. However, a general principle for the combined sampling duration and frequency for different taxa is lacking. In this study, we evaluated whether Nyquist–Shannon sampling theorem applies to accelerometer-based classification of animal behaviour and energy expenditure approximation. To evaluate the influence of accelerometer sampling frequency on behaviour classification, we annotated accelerometer data from seven European pied flycatchers (Ficedula hypoleuca) freely moving in aviaries. We also used simulated data to systematically evaluate the combined effect of sampling duration and sampling frequency on the performance of estimating signal frequency and amplitude. Results: We found that a sampling frequency higher than Nyquist frequency at 100 Hz was needed to classify fast, short-burst behavioural movements of pied flycatcher, such as swallowing food with a mean frequency of 28 Hz. In contrast, high frequency movements with longer durations such as flight could be characterized adequately using much lower sampling frequency of 12.5 Hz. To identify rapid transient prey catching manoeuvres within these flight bouts, again a high frequency sampling at 100 Hz was needed. For both the experimental data of the flycatchers and the simulated data, the combination of sampling frequency and sampling duration affected the accuracy of signal frequency and amplitude estimation. For long sampling durations, the sampling frequency equal to the Nyquist frequency was adequate for accurate signal frequency and amplitude estimation. Accuracy declined with decreasing sampling duration, especially for signal amplitude estimation with up to 40% standard deviation of normalized amplitude difference. To accurately estimate signal amplitude at low sampling duration, a sampling frequency of four times the signal frequency was necessary (two times the Nyquist frequency). Conclusions: The appropriate sampling frequency of accelerometers depends on the objective of the specific study and the characteristics of the behaviour. For studies with no constraints on device battery and storage, a sampling frequency of at least two times the Nyquist frequency will achieve relative optimal representative of signal information (i.e., frequency and amplitude). For classification and energy expenditure estimation of short-burst behaviours, 1.4 times the Nyquist frequency of behaviour is required. [ABSTRACT FROM AUTHOR]

The article discusses the discovery of the mite species Tarsonemus populi Magowski, 2010 in Western Asia, specifically on the new beetle host Trachypteris picta (Buprestidae) in Iran. The study provides new insights into the phoretic associations between mites and beetles in the subgenus Schaarschmidtia, highlighting the diversity of these associations and the potential role of mites as biological control agents for beetle pests.

Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We classified visible and thermal images separately and compared them with the results of image fusion. Fused images provided minimal improvement for cows and horses compared to visible images alone, likely because the size, shape, and color of these species made them conspicuous against the background. For white-tailed deer, which were typically cryptic against their backgrounds and often in shadows in visible images, the added information from thermal images improved detection and classification in fusion methods from 15 to 85%. Our results suggest that image fusion is ideal for surveying animals inconspicuous from their backgrounds, and our approach uses few image pairs to train compared to typical machine-learning methods. We discuss computational and field considerations to improve drone surveys using our fusion approach. [ABSTRACT FROM AUTHOR]