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"Fishes Classification."
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Rapid and repeated evolution of pigmentation patterns in reef fishes
Pigmentation patterns are central to animal biology-shaping camouflage, signaling, and mate selection-and uncovering the mechanisms driving their diversification is key to understanding the evolutionary principles that generate this fundamental dimension of biodiversity. Reef fishes exhibit an incredible variety of patterns, from simple spots to intricate designs. To date, the underlying evolutionary processes that govern their diversification remain unclear.
Here, we investigate the relationship between pigmentation pattern diversity, species richness, and geography across six iconic reef fish families. We provide evidence for a positive correlation between pattern diversity and species richness, with a high divergence of pigmentation patterns in every biogeographic region. Then, by using a suit of phylogenetically informed comparative analyses, we demonstrate that the evolution of pigmentation patterns is characterized by a combination of rapid and constrained phenotypic diversification.
Overall, our findings illuminate factors that explain pigmentation pattern diversity in living reef fishes, revealing that speciation events have driven constant high levels of pigmentation pattern disparity within subclades and across globally variable reef fish assemblages.
Journal Article
Fishes : a guide to their diversity
Offers a broad overview of the morphological diversity of fishes, arranged in a modern classification system.
Book
Novelty Recognition: Fish Species Classification via Open-Set Recognition
To support the sustainable use of marine resources, regulations have been proposed to reduce fish discards focusing on the registration of all listed species. To comply with such regulations, computer vision methods have been developed. Nevertheless, current approaches are constrained by their closed-set nature, where they are designed only to recognize fish species that were present during training. In the real world, however, samples of unknown fish species may appear in different fishing regions or seasons, requiring fish classification to be treated as an open-set problem. This work focuses on the assessment of open-set recognition to automate the registration process of fish. The state-of-the-art Multiple Gaussian Prototype Learning (MGPL) was compared with the simple yet powerful Open-Set Nearest Neighbor (OSNN) and the Probability of Inclusion Support Vector Machine (PISVM). For the experiments, the Fish Detection and Weight Estimation dataset, containing images of 2216 fish instances from nine species, was used. Experimental results demonstrated that OSNN and PISVM outperformed MGPL in both recognizing known and unknown species. OSNN achieved the best results when classifying samples as either one of the known species or as an unknown species with an F1-macro of 0.79±0.05 and an AUROC score of 0.92±0.01 surpassing PISVM by 0.05 and 0.03, respectively.
Journal Article
Adaptive identity-regularized generative adversarial networks with species-specific loss functions for enhanced fish classification and segmentation through data augmentation
Traditional fish classification systems suffer from limited training data and imbalanced datasets, particularly for rare or morphologically complex species. This paper presents a novel Generative Adversarial Network architecture that integrates adaptive identity blocks to preserve critical species-specific features during generation, coupled with species-specific loss functions designed around distinctive characteristics of marine species. Our method introduces adaptive identity blocks that learn to maintain species-invariant features while allowing controlled morphological variations for data augmentation. The species-specific loss function incorporates morphological constraints and taxonomic relationships to ensure generated samples maintain biological plausibility while enhancing dataset diversity. Experimental evaluation on a comprehensive fish dataset containing nine species demonstrated significant performance improvements. Our proposed method achieved 95.1% ± 1.0% classification accuracy, representing a 9.7% improvement over baseline methods and 6.7% improvement over traditional augmentation approaches. While demonstrated on a dataset of 9000 images across nine fish species, these results provide a solid foundation that warrants validation on larger, more taxonomically diverse datasets to establish broader generalizability. Segmentation performance achieved 89.6% ± 1.3% mean Intersection over Union, representing a 12.3% improvement over baseline methods. Critically, our approach showed substantial improvements for morphologically complex species, with expert evaluation by marine biology specialists confirming 88.7% ± 2.0% overall quality and achieving 87.4% ± 1.6% biological validation score. Statistical significance testing confirmed all improvements at
p
< 0.001 with large effect sizes, and cross-validation demonstrated exceptional consistency across folds. The results validate the effectiveness of our biologically-informed approach for generating high-quality synthetic fish data that significantly improves classification and segmentation performance while maintaining biological authenticity.
Journal Article
AI-driven classification and precision cutting algorithms using machine vision in a customer-operated fish processing system
Despite the high nutritional value of fish, it is often under-consumed due to its characteristic odor and laborious cleaning process. This sensory barrier significantly diminishes the appeal of fish, particularly in regions or cultures where individual exhibit heightened sensitivity to fish odor. Fish processing systems have been developed to facilitate cutting and cleaning steps in aquatic supply centers and factories. In this study, to upgrade a fish processing system to an intelligent machine, four high-consumption fish classes were classified using Artificial Intelligence (AI), and the corresponding cutting point determination algorithms were developed using a multipurpose backlighted pure blue background for each class. As the classification algorithms developed, the best results were selected based on the least total MSE value. The best ANN structure was determined as 6–23–4 with 99.62%, 96.72%, and 95.06% with corresponding MSE values of 9.51 × 10
–5
, 2.03 × 10
–2
, and 2.54 × 10
–2
in the train, validation, and test sets, respectively. This structure was recorded as the best one with the ‘Logsig’ function in both hidden and output layers with the LM learning algorithm. The total classification accuracy of the SVM classifier resulted in 99.69% and 98.75%, with the corresponding MSE values of 1.23 × 10
–2
and 1.25 × 10
–2
in train and test data sets, respectively. As soon as the fish were classified, their unique cutting point determination algorithms were applied for fish processing. Finally, the head and belly cutting points accuracy of Silver Carp, Carp, and Trout fish were resulted in 98.36% and 99.49%, 97.85% and 98.07%, and 96.61% and 97.90%, respectively.
Journal Article
Fishes
There are more than 33,000 species of living fishes, accounting for more than half of the extant vertebrate diversity on Earth. This unique and comprehensive reference showcases the basic anatomy and diversity of all 82 orders of fishes and more than 150 of the most commonly encountered families, focusing on their distinctive features. Accurate identification of each group, including its distinguishing characteristics, is supported with clear photographs of preserved specimens, primarily from the archives of the Marine Vertebrate Collection at Scripps Institution of Oceanography. This diagnostic information is supplemented by radiographs, additional illustrations of particularly diverse lineages, and key references and ecological information for each group. An ideal companion to primary ichthyology texts, Fishes: A Guide to Their Diversity gives a broad overview of fish morphology arranged in a modern classification system for students, fisheries scientists, marine biologists, vertebrate zoologists, and everyday naturalists. This survey of the most speciose group of vertebrates on Earth will expand the appreciation of and interest in the amazing diversity of fishes.
eBook
Deep evolutionary origin of limb and fin regeneration
Salamanders and lungfishes are the only sarcopterygians (lobe-finned vertebrates) capable of paired appendage regeneration, regardless of the amputation level. Among actinopterygians (ray-finned fishes), regeneration after amputation at the fin endoskeleton has only been demonstrated in polypterid fishes (Cladistia). Whether this ability evolved independently in sarcopterygians and actinopterygians or has a common origin remains unknown. Here we combine fin regeneration assays and comparative RNA-sequencing (RNA-seq) analysis of Polypterus and axolotl blastemas to provide support for a common origin of paired appendage regeneration in Osteichthyes (bony vertebrates). We show that, in addition to polypterids, regeneration after fin endoskeleton amputation occurs in extant representatives of 2 other nonteleost actinopterygians: the American paddlefish (Chondrostei) and the spotted gar (Holostei). Furthermore, we assessed regeneration in 4 teleost species and show that, with the exception of the blue gourami (Anabantidae), 3 species were capable of regenerating fins after endoskeleton amputation: the white convict and the oscar (Cichlidae), and the goldfish (Cyprinidae). Our comparative RNA-seq analysis of regenerating blastemas of axolotl and Polypterus reveals the activation of common genetic pathways and expression profiles, consistent with a shared genetic program of appendage regeneration. Comparison of RNA-seq data from early Polypterus blastema to single-cell RNA-seq data from axolotl limb bud and limb regeneration stages shows that Polypterus and axolotl share a regeneration-specific genetic program. Collectively, our findings support a deep evolutionary origin of paired appendage regeneration in Osteichthyes and provide an evolutionary framework for studies on the genetic basis of appendage regeneration.
Journal Article
The African coelacanth genome provides insights into tetrapod evolution
The discovery of a living coelacanth specimen in 1938 was remarkable, as this lineage of lobe-finned fish was thought to have become extinct 70 million years ago. The modern coelacanth looks remarkably similar to many of its ancient relatives, and its evolutionary proximity to our own fish ancestors provides a glimpse of the fish that first walked on land. Here we report the genome sequence of the African coelacanth, Latimeria chalumnae. Through a phylogenomic analysis, we conclude that the lungfish, and not the coelacanth, is the closest living relative of tetrapods. Coelacanth protein-coding genes are significantly more slowly evolving than those of tetrapods, unlike other genomic features. Analyses of changes in genes and regulatory elements during the vertebrate adaptation to land highlight genes involved in immunity, nitrogen excretion and the development of fins, tail, ear, eye, brain and olfaction. Functional assays of enhancers involved in the fin-to-limb transition and in the emergence of extra-embryonic tissues show the importance of the coelacanth genome as a blueprint for understanding tetrapod evolution.
Journal Article
Sodium Channel Genes and the Evolution of Diversity in Communication Signals of Electric Fishes: Convergent Molecular Evolution
We investigated whether the evolution of electric organs and electric signal diversity in two independently evolved lineages of electric fishes was accompanied by convergent changes on the molecular level. We found that a sodium channel gene ($Na_{v}1.4a$) that is expressed in muscle in nonelectric fishes has lost its expression in muscle and is expressed instead in the evolutionarily novel electric organ in both lineages of electric fishes. This gene appears to be evolving under positive selection in both lineages, facilitated by its restricted expression in the electric organ. This view is reinforced by the lack of evidence for selection on this gene in one electric species in which expression of this gene is retained in muscle. Amino acid replacements occur convergently in domains that influence channel inactivation, a key trait for shaping electric communication signals. Some amino acid replacements occur at or adjacent to sites at which disease-causing mutations have been mapped in human sodium channel genes, emphasizing that these replacements occur in functionally important domains. Selection appears to have acted on the final step in channel inactivation, but complementarily on the inactivation \"ball\" in one lineage, and its receptor site in the other lineage. Thus, changes in the expression and sequence of the same gene are associated with the independent evolution of signal complexity.
Journal Article