The framework of integrating passive acoustic monitoring (PAM) and deep learning algorithms with social network analysis (SNA) presents a groundbreaking approach to understanding the complex dynamics of animal societies, especially studying the social behavior and communication of elusive species or those living in inaccessible habitats. By leveraging the non-invasive nature of PAM, we could collect long-term, high-resolution audio data of animal vocalizations, which are essential for understanding social interactions. Applying deep learning algorithms to these data has significantly enhanced our ability to identify, classify, and extract subtle patterns within vocalizations, revealing social subgroups and communication networks that were once undetectable. Furthermore, this technological advancement enables the efficient processing of vast amounts of data and the integration of multi-layered information, such as movement and environmental data, to create a comprehensive view of animal social networks. The framework proposed in this review also facilitates the comparison of social networks across different species and ecological contexts, contributing to a deeper understanding of the principles governing social behavior. As technology continues to evolve, the potential of this framework to transform our capacity to study and protect animal societies is immense, offering a promising future for behavioral ecology and conservation biology.

Major ampullate (MA) silk, synthesized by spiders, is tougher than most biological and synthetic materials. Orb weavers evolved some of the toughest MA silk, reaching extremes in bark spiders, genus Caerostris (Araneidae). Increased proline content is associated with tougher silk but may increase the metabolic cost. Transitions (phylogenetic/ontogenetic) to larger body sizes are expected to drive coevolution of tougher, costlier silk, because larger prey presents disproportionally higher kinetic energy. Interspecific shifts to tougher MA silk are documented, but intraspecific patterns are unknown, although spiders increase several hundred times in body mass through ontogeny. Small spiderlings prey on small insects and might not face the selection pressure on adults for capturing large prey. Additionally, extreme female-biased sexual size dimorphism in orb-weaving species like bark spiders results in sex-specific selection pressures for small versus large prey. We therefore ask whether species with exceptionally tough silk, like bark spiders, show different patterns in silk toughness between ontogenetic stages and sexes. We posed three hypotheses: H1, constrained silk production hypothesis; H2, sexually decoupled silk production hypothesis; H3, body size selection pressure hypothesis; and tested them by investigating the mechanical properties of MA silk among size classes and sexes in two Caerostris species from Madagascar, C. darwini Kuntner & Agnarsson, 2010 and C. kuntneri Gregorič & Yu, 2025. We found that only large females produce exceptionally tough silk with higher initial stiffness, while juvenile females and all males produce inferior silks. These results imply ontogenetic plasticity in Caerostris silk production and support the third hypothesis.

A cartoon for showing the two steps (regional dispersal and local dispersal) of the proposed theory for biodiversity formation.

High altitudes are challenging for the animals that inhabit these environments. The Xizang plateau frog (Nanorana parkeri), endemic to the Qinghai-Tibet Plateau and distributed between 2800 and 5100 m, represents an ideal model for studying high-altitude adaptations. Here, we compared environmental differences between high- (4600 m) and low-altitude (3400 m) habitats, characterized the physiological traits of high-altitude frogs, and integrated metabolomic and proteomic data to elucidate adaptive mechanisms to extreme environments. High-altitude habitats exhibited significantly lower water temperatures and dissolved oxygen levels. High-altitude frogs showed a 31%-37% reduction in resting metabolic rate, decreased concentrations of metabolites (glucose and β-hydroxybutyric acid), and 18%-56% lower activities of critical metabolic enzymes. This coordinated metabolic depression is indicative of an energy conservation strategy for surviving at high altitudes. Interestingly, hepatic glycogen (3.1-fold increase) and pyruvate accumulated in high-altitude frogs, suggesting enhanced energy storage and potential antioxidant utilization. Metabolomic profiling further revealed a remodeling of glycerophospholipid, indicating adaptive membrane stabilization. Proteomics analysis identified altered expression of proteins involved in stress response, energy metabolism, and translation, including chaperones (DNAJB6 and DNAJC22) and glutathione peroxidase (GPX4), which may be potential biomarkers for evaluating high-altitude adaptation in ectothermic vertebrates. Collectively, these findings demonstrate that N. parkeri survives in high-altitude environments through a synergistic strategy of metabolic remodeling and protein expression adjustment to optimize energy efficiency and enhance cellular protection. This study provides new insights into the mechanisms by which ectothermic vertebrates adapt to extreme environments.

Parasitism can play a key role in shaping species' adaptability to environmental changes. Understanding how intrinsic traits of bird species influence susceptibility to haemosporidian infection is critical for understanding host-parasite dynamics, especially in biodiverse tropical regions. This study aimed to determine the host traits that influence the probability of haemosporidian infection in birds in a tropical country. We compiled published haemosporidian diagnoses of birds from Colombia and data on ecological, morphological, coloration, and sexual selection (dimorphism and dichromatism) traits. We also calculated an index for habitat specialization. Using phylogenetic generalized linear models (phylo-GLMs), we performed a phylogenetically informed comparative analysis of 115 bird species from different families with diverse characteristics. Our analysis revealed that migratory species, birds with larger body sizes, and those with more colorful plumage had a higher probability of infection. Conversely, habitat specialization was negatively associated with infection risk. Our results are explained in the framework of increased exposure to haemosporidian vectors. However, further studies are needed to better understand the relationship between the traits related to sexual selection and infection. These findings provide valuable insights into host-parasite dynamics in tropical bird communities and help to understand susceptibility factors, considering the potential negative consequences for avian communities.

In this study, we introduce Dual-Branch BioTraitNet, a deep-learning model tailored for trait imputation in small-sample ecological and biological datasets. By combining unsupervised and supervised learning strategies, the model jointly leverages quantitative and qualitative trait information. Its dual-branch architecture enables efficient learning under data-sparse conditions and generalizes well across diverse taxa. On the lizard dataset, the model achieved R2 values of 0.862 for mean body length and 0.67 for average body weight; on the fish dataset, R2 values for maximum body length, minimum spawning temperature, and egg diameter were 0.876, 0.402, and 0.496, respectively. Unlike conventional approaches such as K-nearest neighbors (KNN) and genetic algorithms (and their variants), which are often prone to overfitting or underfitting, BioTraitNet demonstrates strong predictive stability and robustness. This is evident in its consistent avoidance of negative R2 values. Notably, it maintains high accuracy even without incorporating phylogenetic information, making it particularly suitable for scenarios where evolutionary data are missing or uncertain. The proposed framework offers a flexible and reliable solution for addressing missing trait data in ecological and evolutionary research. The computational Python code was available from https://github.com/BB-yu/Dual-Branch-BioTraitNet.

The adaptive evolution of Canidae mitochondrial genomes and their mechanistic association with ecological strategies have long been constrained by insufficient cross-lineage integration and unresolved multidimensional interaction networks. Here, complete mitochondrial genomes from all extant canid species (including 11 newly assembled genomes) were analyzed, revealing highly conserved gene arrangements and lineage-specific codon usage patterns. High-altitude species exhibited atypical initiation codons for ND4L, while boreal species exhibited significant termination codon shifts, and polar specialists had distinct codon optimization profiles. Positive selection analyses identified strong selective pressures on arginine- and leucine-encoding sites, with core oxidative phosphorylation genes demonstrating accelerated adaptive evolution in large-bodied canids and specialized predatory lineages. Phylogenomic reconstructions revealed consistency in South American Lycalopex radiation timing with regional orogenic events, while also linking Canis diversification to grassland biome expansion. Further, statistical models confirmed robust correlations between mitochondrial evolutionary rates and both body mass and predatory ecology, wherein body size increases drive metabolic optimization through lineage-specific selection on energy-related genes. Based on these observations, a "functional constraint-geological driver-body size adaptation" tripartite framework is proposed that highlights how mitochondrial genomes maintain metabolic plasticity through mutation-selection equilibrium, how geological events trigger lineage divergence, and how body size-predation strategies shape modular gene evolution. Consequently, this study establishes a novel paradigm for understanding genome-environment interactions in terrestrial carnivore adaptations.

Sexual dimorphism is classically attributed to sexual selection, yet natural selection via sex-specific ecological pressures is equally important. We investigated this interplay by testing how camouflage and thermoregulation shape sexual color dimorphism across four Diploderma lizards with a comparative framework capturing diverse ecologies. Using spectrometry and image analysis, we documented pronounced sexual color dimorphism in dorsal patterns. Females prioritized background matching, while males favored high-contrast surface disruption, except in Diploderma slowinskii where monomorphic strategies suggested habitat-specific adaptations. Male stripes critical for disruption significantly reduced solar heat gain, imposing a physiological cost absent in females. This sex-specific optimization, males sacrificing thermoregulation for camouflage efficacy and females favoring crypsis, demonstrates how divergent natural selection pressures drive sexual color dimorphism evolution. Our findings enhance the understanding of animal coloration beyond the sexual selection paradigm, positioning ecological trade-off as a fundamental mechanism shaping sexual color dimorphism.

This flowchart outlines the comprehensive workflow of the study, integrating diverse bioinformatics tools (e.g., NCBI2GO, SSU-align, bpRNA) and their sequential interactions. Key steps, such as data preprocessing, structural prediction, and evolutionary analysis, are depicted with their respective outputs (e.g., standardized records, consensus templates, domain annotations) listed on the right, connected via directional arrows to illustrate data flow.

Birds play a critical role in maintaining ecological balance and serve as key indicators of biodiversity. Observing bird behavior in natural environments poses significant challenges. However, identifying bird songs through sensor technology provides a non-invasive and environmentally friendly method for monitoring avian diversity. Nevertheless, bird songs in natural environments are often obscured by substantial noise, and supervised learning-based recognition methods depend on extensive manual data annotation. To address these challenges, we propose Contrastive Residual Masked AutoEncoder-BirdNET (CResMAE-BirdNET), a specialized network for bird song recognition capable of autonomously extracting features from vast amounts of unlabeled acoustic data, thereby significantly enhancing recognition performance. First, to mitigate environmental noise and enhance model robustness, we apply four audio enhancement techniques and introduce a time-frequency self-calibration fusion module (TFSC) that integrates spectral ripple features. Next, CResMAE-BirdNET combines contrastive learning with a masked autoencoder framework, integrating residual attention in the encoder and a residual multi-layer perceptron in the decoder, enhancing the ability to capture the relationship between local and global features for superior feature representation. Finally, extensive experiments on our self-built 40-class dataset (Bird40Song) and the public dataset (Birdsdata) validate the effectiveness of the proposed method, achieving recognition accuracies of 99.35% and 98.43%, along with F1-scores of 99.34% and 98.28%, respectively. The results highlight significant advancements in bird song recognition, demonstrating the potential of CResMAE-BirdNET to support large-scale ecological monitoring and biodiversity research. Code available at: https://github.com/xzq-okkkkkkk/CResMAE-BirdNET.