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Simović P, Milosavljević A, Stojanović K, Radenković M, Savić-Zdravković D, Predić B, Petrović A, Božanić M, Milošević D. Automated identification of aquatic insects: A case study using deep learning and computer vision techniques. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 935:172877. [PMID: 38740196 DOI: 10.1016/j.scitotenv.2024.172877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/27/2024] [Indexed: 05/16/2024]
Abstract
Deep learning techniques have recently found application in biodiversity research. Mayflies (Ephemeroptera), stoneflies (Plecoptera) and caddisflies (Trichoptera), often abbreviated as EPT, are frequently used for freshwater biomonitoring due to their large numbers and sensitivity to environmental changes. However, the morphological identification of EPT species is a challenging but fundamental task. Morphological identification of these freshwater insects is therefore not only extremely time-consuming and costly, but also often leads to misjudgments or generates datasets with low taxonomic resolution. Here, we investigated the application of deep learning to increase the efficiency and taxonomic resolution of biomonitoring programs. Our database contains 90 EPT taxa (genus or species level), with the number of images per category ranging from 21 to 300 (16,650 in total). Upon completion of training, a CNN (Convolutional Neural Network) model was created, capable of automatically classifying these taxa into their appropriate taxonomic categories with an accuracy of 98.7 %. Our model achieved a perfect classification rate of 100 % for 68 of the taxa in our dataset. We achieved noteworthy classification accuracy with morphologically closely related taxa within the training data (e.g., species of the genus Baetis, Hydropsyche, Perla). Gradient-weighted Class Activation Mapping (Grad-CAM) visualized the morphological features responsible for the classification of the treated species in the CNN models. Within Ephemeroptera, the head was the most important feature, while the thorax and abdomen were equally important for the classification of Plecoptera taxa. For the order Trichoptera, the head and thorax were almost equally important. Our database is recognized as the most extensive aquatic insect database, notably distinguished by its wealth of included categories (taxa). Our approach can help solve long-standing challenges in biodiversity research and address pressing issues in monitoring programs by saving time in sample identification.
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Affiliation(s)
- Predrag Simović
- Department of Biology and Ecology, Faculty of Science, University of Kragujevac, Radoja Domanovića 12, 34000 Kragujevac, Serbia.
| | - Aleksandar Milosavljević
- Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia.
| | - Katarina Stojanović
- Department of Zoology, Faculty of Biology, University of Belgrade, Studentski trg 16, Belgrade, Serbia.
| | - Milena Radenković
- Department of Biology and Ecology, Faculty of Science, University of Kragujevac, Radoja Domanovića 12, 34000 Kragujevac, Serbia.
| | - Dimitrija Savić-Zdravković
- Department of Biology and Ecology, Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, 18000 Niš, Serbia.
| | - Bratislav Predić
- Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia.
| | - Ana Petrović
- Department of Biology and Ecology, Faculty of Science, University of Kragujevac, Radoja Domanovića 12, 34000 Kragujevac, Serbia.
| | - Milenka Božanić
- Department of Zoology, Faculty of Biology, University of Belgrade, Studentski trg 16, Belgrade, Serbia.
| | - Djuradj Milošević
- Department of Biology and Ecology, Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, 18000 Niš, Serbia.
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2
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Karbstein K, Kösters L, Hodač L, Hofmann M, Hörandl E, Tomasello S, Wagner ND, Emerson BC, Albach DC, Scheu S, Bradler S, de Vries J, Irisarri I, Li H, Soltis P, Mäder P, Wäldchen J. Species delimitation 4.0: integrative taxonomy meets artificial intelligence. Trends Ecol Evol 2024:S0169-5347(23)00296-3. [PMID: 38849221 DOI: 10.1016/j.tree.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/20/2023] [Accepted: 11/08/2023] [Indexed: 06/09/2024]
Abstract
Although species are central units for biological research, recent findings in genomics are raising awareness that what we call species can be ill-founded entities due to solely morphology-based, regional species descriptions. This particularly applies to groups characterized by intricate evolutionary processes such as hybridization, polyploidy, or asexuality. Here, challenges of current integrative taxonomy (genetics/genomics + morphology + ecology, etc.) become apparent: different favored species concepts, lack of universal characters/markers, missing appropriate analytical tools for intricate evolutionary processes, and highly subjective ranking and fusion of datasets. Now, integrative taxonomy combined with artificial intelligence under a unified species concept can enable automated feature learning and data integration, and thus reduce subjectivity in species delimitation. This approach will likely accelerate revising and unraveling eukaryotic biodiversity.
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Affiliation(s)
- Kevin Karbstein
- Max Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, 07745 Jena, Germany.
| | - Lara Kösters
- Max Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, 07745 Jena, Germany
| | - Ladislav Hodač
- Max Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, 07745 Jena, Germany
| | - Martin Hofmann
- Technical University of Ilmenau, Institute for Computer and Systems Engineering, 98693 Ilmenau, Germany
| | - Elvira Hörandl
- University of Göttingen, Albrecht-von-Haller Institute for Plant Sciences, Department of Systematics, Biodiversity and Evolution of Plants (with Herbarium), 37073 Göttingen, Germany
| | - Salvatore Tomasello
- University of Göttingen, Albrecht-von-Haller Institute for Plant Sciences, Department of Systematics, Biodiversity and Evolution of Plants (with Herbarium), 37073 Göttingen, Germany
| | - Natascha D Wagner
- University of Göttingen, Albrecht-von-Haller Institute for Plant Sciences, Department of Systematics, Biodiversity and Evolution of Plants (with Herbarium), 37073 Göttingen, Germany
| | - Brent C Emerson
- Institute of Natural Products and Agrobiology (IPNA-CSIC), Island Ecology and Evolution Research Group, 38206 La Laguna, Tenerife, Canary Islands, Spain
| | - Dirk C Albach
- Carl von Ossietzky-Universität Oldenburg, Institute of Biology and Environmental Science, 26129 Oldenburg, Germany
| | - Stefan Scheu
- University of Göttingen, Johann-Friedrich-Blumenbach Institute of Zoology and Anthropology, 37073 Göttingen, Germany; University of Göttingen, Centre of Biodiversity and Sustainable Land Use (CBL), 37073 Göttingen, Germany
| | - Sven Bradler
- University of Göttingen, Johann-Friedrich-Blumenbach Institute of Zoology and Anthropology, 37073 Göttingen, Germany
| | - Jan de Vries
- University of Göttingen, Institute for Microbiology and Genetics, Department of Applied Bioinformatics, 37077 Göttingen, Germany; University of Göttingen, Campus Institute Data Science (CIDAS), 37077 Göttingen, Germany; University of Göttingen, Göttingen Center for Molecular Biosciences (GZMB), Department of Applied Bioinformatics, 37077 Göttingen, Germany
| | - Iker Irisarri
- Leibniz Institute for the Analysis of Biodiversity Change (LIB), Centre for Molecular Biodiversity Research, Phylogenomics Section, Museum of Nature, 20146 Hamburg, Germany
| | - He Li
- Eastern China Conservation Centre for Wild Endangered Plant Resources, Chenshan Botanical Garden, 201602 Shanghai, China
| | - Pamela Soltis
- University of Florida, Florida Museum of Natural History, 32611 Gainesville, USA
| | - Patrick Mäder
- Technical University of Ilmenau, Institute for Computer and Systems Engineering, 98693 Ilmenau, Germany; German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany; Friedrich Schiller University Jena, Faculty of Biological Sciences, Institute of Ecology and Evolution, Philosophenweg 16, 07743 Jena, Germany
| | - Jana Wäldchen
- Max Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, 07745 Jena, Germany; German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany
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3
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Shirali H, Hübner J, Both R, Raupach M, Reischl M, Schmidt S, Pylatiuk C. Image-based recognition of parasitoid wasps using advanced neural networks. INVERTEBR SYST 2024; 38:IS24011. [PMID: 38838190 DOI: 10.1071/is24011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/08/2024] [Indexed: 06/07/2024]
Abstract
Hymenoptera has some of the highest diversity and number of individuals among insects. Many of these species potentially play key roles as food sources, pest controllers and pollinators. However, little is known about the diversity and biology and ~80% of the species have not yet been described. Classical taxonomy based on morphology is a rather slow process but DNA barcoding has already brought considerable progress in identification. Innovative methods such as image-based identification and automation can further speed up the process. We present a proof of concept for image data recognition of a parasitic wasp family, the Diapriidae (Hymenoptera), obtained as part of the GBOL III project. These tiny (1.2-4.5mm) wasps were photographed and identified using DNA barcoding to provide a solid ground truth for training a neural network. Taxonomic identification was used down to the genus level. Subsequently, three different neural network architectures were trained, evaluated and optimised. As a result, 11 different genera of diaprids and one mixed group of 'other Hymenoptera' can be classified with an average accuracy of 96%. Additionally, the sex of the specimen can be classified automatically with an accuracy of >97%.
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Affiliation(s)
- Hossein Shirali
- Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), D-76149 Karlsruhe, Germany
| | - Jeremy Hübner
- Zoologische Staatssammlung München, Münchhausenstraße 21, D-81247 Munich, Germany
| | - Robin Both
- Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), D-76149 Karlsruhe, Germany
| | - Michael Raupach
- Zoologische Staatssammlung München, Münchhausenstraße 21, D-81247 Munich, Germany
| | - Markus Reischl
- Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), D-76149 Karlsruhe, Germany
| | - Stefan Schmidt
- Deceased. Formerly at Zoologische Staatssammlung München, Münchhausenstraße 21, D-81247 Munich, Germany
| | - Christian Pylatiuk
- Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), D-76149 Karlsruhe, Germany
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Crowson M, Williams J, Sharma J, Pettorelli N. Challenges for monitoring artificial turf expansion with satellite remote sensing. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:580. [PMID: 38805109 DOI: 10.1007/s10661-024-12724-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/17/2024] [Indexed: 05/29/2024]
Abstract
Urban green spaces are central components of urban ecosystems, providing refuge for wildlife while helping 'future proof' cities against climate change. Conversion of urban green spaces to artificial turf has become increasingly popular in various developed countries, such as the UK, leading to reduced urban ecosystem services delivery. To date, there is no established satellite remote sensing method for reliably detecting and mapping artificial turf expansion at scale. We here assess the combined use of very high-resolution multispectral satellite imagery and classical, open source, supervised classification approaches to map artificial lawns in a typical British city. Both object-based and pixel-based classifications struggled to reliably detect artificial turf, with large patches of artificial turf not being any more reliably identified than small patches of artificial turf. As urban ecosystems are increasingly recognised for their key contributions to human wellbeing and health, the poor performance of these standard methods highlights the urgency of developing and applying new, easily accessible approaches for the monitoring of these important ecosystems.
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Affiliation(s)
- Merry Crowson
- Institute of Zoology, Zoological Society of London, Regent's Park, London, NW1 4RY, UK
- Department of Geography and Environmental Science, University of Reading, Reading, RG6 6UR, UK
| | - Jake Williams
- Institute of Zoology, Zoological Society of London, Regent's Park, London, NW1 4RY, UK
- Department of Life Sciences, Imperial College London, Buckhurst Road, Ascot, SL5 7PY, UK
| | - James Sharma
- Institute of Zoology, Zoological Society of London, Regent's Park, London, NW1 4RY, UK
| | - Nathalie Pettorelli
- Institute of Zoology, Zoological Society of London, Regent's Park, London, NW1 4RY, UK.
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Spiesman BJ, Gratton C, Gratton E, Hines H. Deep learning for identifying bee species from images of wings and pinned specimens. PLoS One 2024; 19:e0303383. [PMID: 38805521 PMCID: PMC11132477 DOI: 10.1371/journal.pone.0303383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 04/23/2024] [Indexed: 05/30/2024] Open
Abstract
One of the most challenging aspects of bee ecology and conservation is species-level identification, which is costly, time consuming, and requires taxonomic expertise. Recent advances in the application of deep learning and computer vision have shown promise for identifying large bumble bee (Bombus) species. However, most bees, such as sweat bees in the genus Lasioglossum, are much smaller and can be difficult, even for trained taxonomists, to identify. For this reason, the great majority of bees are poorly represented in the crowdsourced image datasets often used to train computer vision models. But even larger bees, such as bumble bees from the B. vagans complex, can be difficult to separate morphologically. Using images of specimens from our research collections, we assessed how deep learning classification models perform on these more challenging taxa, qualitatively comparing models trained on images of whole pinned specimens or on images of bee forewings. The pinned specimen and wing image datasets represent 20 and 18 species from 6 and 4 genera, respectively, and were used to train the EfficientNetV2L convolutional neural network. Mean test precision was 94.9% and 98.1% for pinned and wing images respectively. Results show that computer vision holds great promise for classifying smaller, more difficult to identify bees that are poorly represented in crowdsourced datasets. Images from research and museum collections will be valuable for expanding classification models to include additional species, which will be essential for large scale conservation monitoring efforts.
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Affiliation(s)
- Brian J. Spiesman
- Department of Entomology, Kansas State University, Manhattan, Kansas, United States of America
| | - Claudio Gratton
- Department of Entomology, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Elena Gratton
- Department of Entomology, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America
| | - Heather Hines
- Department of Entomology, Penn State University, State College, Pennsylvania, United States of America
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6
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Sittinger M, Uhler J, Pink M, Herz A. Insect detect: An open-source DIY camera trap for automated insect monitoring. PLoS One 2024; 19:e0295474. [PMID: 38568922 PMCID: PMC10990185 DOI: 10.1371/journal.pone.0295474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/28/2024] [Indexed: 04/05/2024] Open
Abstract
Insect monitoring is essential to design effective conservation strategies, which are indispensable to mitigate worldwide declines and biodiversity loss. For this purpose, traditional monitoring methods are widely established and can provide data with a high taxonomic resolution. However, processing of captured insect samples is often time-consuming and expensive, which limits the number of potential replicates. Automated monitoring methods can facilitate data collection at a higher spatiotemporal resolution with a comparatively lower effort and cost. Here, we present the Insect Detect DIY (do-it-yourself) camera trap for non-invasive automated monitoring of flower-visiting insects, which is based on low-cost off-the-shelf hardware components combined with open-source software. Custom trained deep learning models detect and track insects landing on an artificial flower platform in real time on-device and subsequently classify the cropped detections on a local computer. Field deployment of the solar-powered camera trap confirmed its resistance to high temperatures and humidity, which enables autonomous deployment during a whole season. On-device detection and tracking can estimate insect activity/abundance after metadata post-processing. Our insect classification model achieved a high top-1 accuracy on the test dataset and generalized well on a real-world dataset with captured insect images. The camera trap design and open-source software are highly customizable and can be adapted to different use cases. With custom trained detection and classification models, as well as accessible software programming, many possible applications surpassing our proposed deployment method can be realized.
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Affiliation(s)
- Maximilian Sittinger
- Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Biological Control, Dossenheim, Germany
| | - Johannes Uhler
- Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Biological Control, Dossenheim, Germany
| | - Maximilian Pink
- Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Biological Control, Dossenheim, Germany
| | - Annette Herz
- Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Biological Control, Dossenheim, Germany
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Raju C, Elpa DP, Urban PL. Automation and Computerization of (Bio)sensing Systems. ACS Sens 2024; 9:1033-1048. [PMID: 38363106 PMCID: PMC10964247 DOI: 10.1021/acssensors.3c01887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/21/2023] [Accepted: 01/29/2024] [Indexed: 02/17/2024]
Abstract
Sensing systems necessitate automation to reduce human effort, increase reproducibility, and enable remote sensing. In this perspective, we highlight different types of sensing systems with elements of automation, which are based on flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. Finally, we discuss the role of computer technology in sensing systems. Automated flow injection and sequential injection techniques offer precise and efficient sample handling and dependable outcomes. They enable continuous analysis of numerous samples, boosting throughput, and saving time and resources. They enhance safety by minimizing contact with hazardous chemicals. Microfluidic systems are enhanced by automation to enable precise control of parameters and increase of analysis speed. Robotic sampling and sample preparation platforms excel in precise execution of intricate, repetitive tasks such as sample handling, dilution, and transfer. These platforms enhance efficiency by multitasking, use minimal sample volumes, and they seamlessly integrate with analytical instruments. Other sensor prototypes utilize mechanical devices and computer technology to address real-world issues, offering efficient, accurate, and economical real-time solutions for analyte identification and quantification in remote areas. Computer technology is crucial in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from the sensor data, supporting the Internet of Things with efficient data management.
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Affiliation(s)
- Chamarthi
Maheswar Raju
- Department of Chemistry, National
Tsing Hua University 101, Section 2, Kuang-Fu Rd., Hsinchu 300044, Taiwan
| | - Decibel P. Elpa
- Department of Chemistry, National
Tsing Hua University 101, Section 2, Kuang-Fu Rd., Hsinchu 300044, Taiwan
| | - Pawel L. Urban
- Department of Chemistry, National
Tsing Hua University 101, Section 2, Kuang-Fu Rd., Hsinchu 300044, Taiwan
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8
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Adjei KP, Finstad AG, Koch W, O'Hara RB. Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance. Ecol Evol 2024; 14:e11092. [PMID: 38455149 PMCID: PMC10918728 DOI: 10.1002/ece3.11092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/12/2024] [Accepted: 02/18/2024] [Indexed: 03/09/2024] Open
Abstract
Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.
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Affiliation(s)
- Kwaku Peprah Adjei
- Department of Mathematical SciencesNorwegian University of Science and TechnologyTrondheimNorway
- Center for Biodiversity DynamicsNorwegian University of Science and TechnologyTrondheimNorway
- Norwegian Institute for Nature ResearchTrondheimNorway
| | - Anders Gravbrøt Finstad
- Center for Biodiversity DynamicsNorwegian University of Science and TechnologyTrondheimNorway
- Department of Natural HistoryNorwegian University of Science and TechnologyTrondheimNorway
| | - Wouter Koch
- Center for Biodiversity DynamicsNorwegian University of Science and TechnologyTrondheimNorway
- Norwegian Biodiversity Information CentreTrondheimNorway
| | - Robert Brian O'Hara
- Department of Mathematical SciencesNorwegian University of Science and TechnologyTrondheimNorway
- Center for Biodiversity DynamicsNorwegian University of Science and TechnologyTrondheimNorway
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Hernández-López R, Travieso-González CM. Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches. SENSORS (BASEL, SWITZERLAND) 2024; 24:1372. [PMID: 38474908 DOI: 10.3390/s24051372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/29/2024] [Accepted: 02/17/2024] [Indexed: 03/14/2024]
Abstract
The Canary Islands are considered a hotspot of biodiversity and have high levels of endemicity, including endemic reptile species. Nowadays, some invasive alien species of reptiles are proliferating with no control in different parts of the territory, creating a dangerous situation for the ecosystems of this archipelago. Despite the fact that the regional authorities have initiated actions to try to control the proliferation of invasive species, the problem has not been solved as it depends on sporadic sightings, and it is impossible to determine when these species appear. Since no studies for automatically identifying certain species of reptiles endemic to the Canary Islands have been found in the current state-of-the-art, from the Signals and Communications Department of the Las Palmas de Gran Canaria University (ULPGC), we consider the possibility of developing a detection system based on automatic species recognition using deep learning (DL) techniques. So this research conducts an initial identification study of some species of interest by implementing different neural network models based on transfer learning approaches. This study concludes with a comparison in which the best performance is achieved by integrating the EfficientNetV2B3 base model, which has a mean Accuracy of 98.75%.
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Affiliation(s)
- Ruymán Hernández-López
- Signals and Communications Department (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain
| | - Carlos M Travieso-González
- Signals and Communications Department (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain
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10
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Dalbosco Dell'Aglio D, Rivas-Sánchez DF, Wright DS, Merrill RM, Montgomery SH. The Sensory Ecology of Speciation. Cold Spring Harb Perspect Biol 2024; 16:a041428. [PMID: 38052495 PMCID: PMC10759811 DOI: 10.1101/cshperspect.a041428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
In this work, we explore the potential influence of sensory ecology on speciation, including but not limited to the concept of sensory drive, which concerns the coevolution of signals and sensory systems with the local environment. The sensory environment can influence individual fitness in a variety of ways, thereby affecting the evolution of both pre- and postmating reproductive isolation. Previous work focused on sensory drive has undoubtedly advanced the field, but we argue that it may have also narrowed our understanding of the broader influence of the sensory ecology on speciation. Moreover, the clearest examples of sensory drive are largely limited to aquatic organisms, which may skew the influence of contributing factors. We review the evidence for sensory drive across environmental conditions, and in this context discuss the importance of more generalized effects of sensory ecology on adaptive behavioral divergence. Finally, we consider the potential of rapid environmental change to influence reproductive barriers related to sensory ecologies. Our synthesis shows the importance of sensory conditions for local adaptation and divergence in a range of behavioral contexts and extends our understanding of the interplay between sensory ecology and speciation.
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Affiliation(s)
- Denise Dalbosco Dell'Aglio
- School of Biological Science, University of Bristol, Bristol BS8 1TQ, United Kingdom
- Smithsonian Tropical Research Institute, Gamboa 0843-03092, Panama
| | - David F Rivas-Sánchez
- School of Biological Science, University of Bristol, Bristol BS8 1TQ, United Kingdom
| | - Daniel Shane Wright
- Faculty of Biology, Division of Evolutionary Biology, LMU Munich, 82152 Planegg-Martinsried, Germany
| | - Richard M Merrill
- Smithsonian Tropical Research Institute, Gamboa 0843-03092, Panama
- Faculty of Biology, Division of Evolutionary Biology, LMU Munich, 82152 Planegg-Martinsried, Germany
| | - Stephen H Montgomery
- School of Biological Science, University of Bristol, Bristol BS8 1TQ, United Kingdom
- Smithsonian Tropical Research Institute, Gamboa 0843-03092, Panama
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11
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Cantwell-Jones A, Tylianakis JM, Larson K, Gill RJ. Using individual-based trait frequency distributions to forecast plant-pollinator network responses to environmental change. Ecol Lett 2024; 27:e14368. [PMID: 38247047 DOI: 10.1111/ele.14368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 01/23/2024]
Abstract
Determining how and why organisms interact is fundamental to understanding ecosystem responses to future environmental change. To assess the impact on plant-pollinator interactions, recent studies have examined how the effects of environmental change on individual interactions accumulate to generate species-level responses. Here, we review recent developments in using plant-pollinator networks of interacting individuals along with their functional traits, where individuals are nested within species nodes. We highlight how these individual-level, trait-based networks connect intraspecific trait variation (as frequency distributions of multiple traits) with dynamic responses within plant-pollinator communities. This approach can better explain interaction plasticity, and changes to interaction probabilities and network structure over spatiotemporal or other environmental gradients. We argue that only through appreciating such trait-based interaction plasticity can we accurately forecast the potential vulnerability of interactions to future environmental change. We follow this with general guidance on how future studies can collect and analyse high-resolution interaction and trait data, with the hope of improving predictions of future plant-pollinator network responses for targeted and effective conservation.
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Affiliation(s)
- Aoife Cantwell-Jones
- Georgina Mace Centre for The Living Planet, Department of Life Sciences, Silwood Park, Imperial College London, Ascot, UK
| | - Jason M Tylianakis
- Georgina Mace Centre for The Living Planet, Department of Life Sciences, Silwood Park, Imperial College London, Ascot, UK
- Bioprotection Aotearoa, School of Biological Sciences, Private Bag 4800, University of Canterbury, Christchurch, New Zealand
| | - Keith Larson
- Climate Impacts Research Centre, Department of Ecology and Environmental Sciences, Umeå University, Umeå, Sweden
| | - Richard J Gill
- Georgina Mace Centre for The Living Planet, Department of Life Sciences, Silwood Park, Imperial College London, Ascot, UK
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12
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Huang X, Rymbekova A, Dolgova O, Lao O, Kuhlwilm M. Harnessing deep learning for population genetic inference. Nat Rev Genet 2024; 25:61-78. [PMID: 37666948 DOI: 10.1038/s41576-023-00636-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 09/06/2023]
Abstract
In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.
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Affiliation(s)
- Xin Huang
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria.
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Vienna, Austria.
| | - Aigerim Rymbekova
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Vienna, Austria
| | - Olga Dolgova
- Integrative Genomics Laboratory, CIC bioGUNE - Centro de Investigación Cooperativa en Biociencias, Derio, Biscaya, Spain
| | - Oscar Lao
- Institute of Evolutionary Biology, CSIC-Universitat Pompeu Fabra, Barcelona, Spain.
| | - Martin Kuhlwilm
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria.
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Vienna, Austria.
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13
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Ahrens D. Species Diagnosis and DNA Taxonomy. Methods Mol Biol 2024; 2744:33-52. [PMID: 38683310 DOI: 10.1007/978-1-0716-3581-0_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
The use of DNA has helped to improve and speed up species identification and delimitation. However, it also provides new challenges to taxonomists. Incongruence of outcome from various markers and delimitation methods, bias from sampling and skewed species distribution, implemented models, and the choice of methods/priors may mislead results and also may, in conclusion, increase elements of subjectivity in species taxonomy. The lack of direct diagnostic outcome from most contemporary molecular delimitation approaches and the need for a reference to existing and best sampled trait reference systems reveal the need for refining the criteria of species diagnosis and diagnosability in the current framework of nomenclature codes and good practices to avoid nomenclatorial instability, parallel taxonomies, and consequently more and new taxonomic impediment.
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Affiliation(s)
- Dirk Ahrens
- Museum A. Koenig Bonn, Leibniz Institute for the Analysis of Biodiversity Change, Bonn, Germany.
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14
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Lambert S, Voznica J, Morlon H. Deep Learning from Phylogenies for Diversification Analyses. Syst Biol 2023; 72:1262-1279. [PMID: 37556735 DOI: 10.1093/sysbio/syad044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 06/20/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023] Open
Abstract
Birth-death (BD) models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood formula must be established each time a new model is proposed; for some models, such a formula is not even tractable. Deep learning can bring solutions in such situations, as deep neural networks can be trained to learn the relation between simulations and parameter values as a regression problem. In this paper, we adapt a recently developed deep learning method from pathogen phylodynamics to the case of diversification inference, and we extend its applicability to the case of the inference of state-dependent diversification models from phylogenies associated with trait data. We demonstrate the accuracy and time efficiency of the approach for the time-constant homogeneous BD model and the Binary-State Speciation and Extinction model. Finally, we illustrate the use of the proposed inference machinery by reanalyzing a phylogeny of primates and their associated ecological role as seed dispersers. Deep learning inference provides at least the same accuracy as likelihood-based inference while being faster by several orders of magnitude, offering a promising new inference approach for the deployment of future models in the field.
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Affiliation(s)
- Sophia Lambert
- Institut de Biologie de l'École Normale Supérieure, École Normale Supérieure, CNRS, INSERM, Université Paris Sciences et Lettres, 46 Rue d'Ulm, 75005 Paris, France
- Institute of Ecology and Evolution, Department of Biology, 5289 University of Oregon, Eugene, OR 97403, USA
| | - Jakub Voznica
- Institut Pasteur, Université Paris Cité, Unité Bioinformatique Evolutive, 25-28 Rue du Dr Roux, 75015 Paris, France
- Unité de Biologie Computationnelle, USR 3756 CNRS, 25-28 Rue du Dr Roux, 75015 Paris, France
| | - Hélène Morlon
- Institut de Biologie de l'École Normale Supérieure, École Normale Supérieure, CNRS, INSERM, Université Paris Sciences et Lettres, 46 Rue d'Ulm, 75005 Paris, France
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15
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Alexander Pyron R. Unsupervised machine learning for species delimitation, integrative taxonomy, and biodiversity conservation. Mol Phylogenet Evol 2023; 189:107939. [PMID: 37804960 DOI: 10.1016/j.ympev.2023.107939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/25/2023] [Accepted: 10/04/2023] [Indexed: 10/09/2023]
Abstract
Integrative taxonomy, combining data from multiple axes of biologically relevant variation, is a major goal of systematics. Ideally, such taxonomies will derive from similarly integrative species-delimitation analyses. Yet, most current methods rely solely or primarily on molecular data, with other layers often incorporated only in a post hoc qualitative or comparative manner. A major limitation is the difficulty of devising quantitative parametric models linking different datasets in a unified ecological and evolutionary framework. Machine Learning (ML) methods offer flexibility in this arena by easily learning high-dimensional associations between observations (e.g., individual specimens) across a wide array of input features (e.g., genetics, geography, environment, and phenotype) to delimit statistically meaningful clusters. Here, I implement an unsupervised method using Self-Organizing (or "Kohonen") Maps (SOMs) for such purposes. Recent extensions called "SuperSOMs" can integrate multiple layers, each of which exerts independent influence on a two-dimensional output grid via empirically estimated weights. The grid cells are then delimited into K distinct units that can be interpreted as species or other entities. I show empirical examples in salamanders (Desmognathus) and snakes (Storeria) with layers representing alleles, space, climate, and traits. Simulations reveal that the SuperSOM approach can detect K = 1, tends not to over-split, reflects contributions from all layers, and limits large layers (e.g., genetic matrices) from overwhelming other datasets, desirable properties addressing major concerns from previous studies. Finally, I suggest that these and similar methods could integrate conservation-relevant layers such as population trends and human encroachment to delimit management units from an explicitly quantitative framework grounded in the ecology and evolution of species limits and boundaries.
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Affiliation(s)
- R Alexander Pyron
- Department of Biological Sciences, The George Washington University, Washington, DC 20052 USA.
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16
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Ling MH, Ivorra T, Heo CC, Wardhana AH, Hall MJR, Tan SH, Mohamed Z, Khang TF. Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species. MEDICAL AND VETERINARY ENTOMOLOGY 2023; 37:767-781. [PMID: 37477152 DOI: 10.1111/mve.12682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 07/03/2023] [Indexed: 07/22/2023]
Abstract
In medical, veterinary and forensic entomology, the ease and affordability of image data acquisition have resulted in whole-image analysis becoming an invaluable approach for species identification. Krawtchouk moment invariants are a classical mathematical transformation that can extract local features from an image, thus allowing subtle species-specific biological variations to be accentuated for subsequent analyses. We extracted Krawtchouk moment invariant features from binarised wing images of 759 male fly specimens from the Calliphoridae, Sarcophagidae and Muscidae families (13 species and a species variant). Subsequently, we trained the Generalized, Unbiased, Interaction Detection and Estimation random forests classifier using linear discriminants derived from these features and inferred the species identity of specimens from the test samples. Fivefold cross-validation results show a 98.56 ± 0.38% (standard error) mean identification accuracy at the family level and a 91.04 ± 1.33% mean identification accuracy at the species level. The mean F1-score of 0.89 ± 0.02 reflects good balance of precision and recall properties of the model. The present study consolidates findings from previous small pilot studies of the usefulness of wing venation patterns for inferring species identities. Thus, the stage is set for the development of a mature data analytic ecosystem for routine computer image-based identification of fly species that are of medical, veterinary and forensic importance.
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Affiliation(s)
- Min Hao Ling
- Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Tania Ivorra
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia
- Department of Environmental Sciences and Natural Resources, University of Alicante, Alicante, Spain
| | - Chong Chin Heo
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia
| | - April Hari Wardhana
- Research Center for Veterinary Science, The National Research and Innovation Agency, Bogor, Indonesia
- Faculty of Veterinary Medicine, Airlangga University, Surabaya, Indonesia
| | | | - Siew Hwa Tan
- International Department of Dipterology, Kuala Lumpur Laboratory, Kuala Lumpur, Malaysia
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Zulqarnain Mohamed
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Tsung Fei Khang
- Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
- Universiti Malaya Centre for Data Analytics, Universiti Malaya, Kuala Lumpur, Malaysia
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17
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Panigrahi S, Maski P, Thondiyath A. Real-time biodiversity analysis using deep-learning algorithms on mobile robotic platforms. PeerJ Comput Sci 2023; 9:e1502. [PMID: 37705641 PMCID: PMC10495972 DOI: 10.7717/peerj-cs.1502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 07/04/2023] [Indexed: 09/15/2023]
Abstract
Ecological biodiversity is declining at an unprecedented rate. To combat such irreversible changes in natural ecosystems, biodiversity conservation initiatives are being conducted globally. However, the lack of a feasible methodology to quantify biodiversity in real-time and investigate population dynamics in spatiotemporal scales prevents the use of ecological data in environmental planning. Traditionally, ecological studies rely on the census of an animal population by the "capture, mark and recapture" technique. In this technique, human field workers manually count, tag and observe tagged individuals, making it time-consuming, expensive, and cumbersome to patrol the entire area. Recent research has also demonstrated the potential for inexpensive and accessible sensors for ecological data monitoring. However, stationary sensors collect localised data which is highly specific on the placement of the setup. In this research, we propose the methodology for biodiversity monitoring utilising state-of-the-art deep learning (DL) methods operating in real-time on sample payloads of mobile robots. Such trained DL algorithms demonstrate a mean average precision (mAP) of 90.51% in an average inference time of 67.62 milliseconds within 6,000 training epochs. We claim that the use of such mobile platform setups inferring real-time ecological data can help us achieve our goal of quick and effective biodiversity surveys. An experimental test payload is fabricated, and online as well as offline field surveys are conducted, validating the proposed methodology for species identification that can be further extended to geo-localisation of flora and fauna in any ecosystem.
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Affiliation(s)
- Siddhant Panigrahi
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Prajwal Maski
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Asokan Thondiyath
- Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
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18
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Pantel JH, Becks L. Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics. Trends Ecol Evol 2023; 38:760-772. [PMID: 37437547 DOI: 10.1016/j.tree.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 07/14/2023]
Abstract
While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.
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Affiliation(s)
- Jelena H Pantel
- Ecological Modelling, Faculty of Biology, University of Duisburg-Essen, Universitätsstraße 2, 45117 Essen, Germany.
| | - Lutz Becks
- University of Konstanz, Aquatic Ecology and Evolution, Limnological Institute University of Konstanz Mainaustraße 252 78464, Konstanz/Egg, Germany
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19
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Batz P, Will T, Thiel S, Ziesche TM, Joachim C. From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring. FRONTIERS IN PLANT SCIENCE 2023; 14:1150748. [PMID: 37538063 PMCID: PMC10396399 DOI: 10.3389/fpls.2023.1150748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 06/26/2023] [Indexed: 08/05/2023]
Abstract
Insect monitoring has gained global public attention in recent years in the context of insect decline and biodiversity loss. Monitoring methods that can collect samples over a long period of time and independently of human influences are of particular importance. While these passive collection methods, e.g. suction traps, provide standardized and comparable data sets, the time required to analyze the large number of samples and trapped specimens is high. Another challenge is the necessary high level of taxonomic expertise required for accurate specimen processing. These factors create a bottleneck in specimen processing. In this context, machine learning, image recognition and artificial intelligence have emerged as promising tools to address the shortcomings of manual identification and quantification in the analysis of such trap catches. Aphids are important agricultural pests that pose a significant risk to several important crops and cause high economic losses through feeding damage and transmission of plant viruses. It has been shown that long-term monitoring of migrating aphids using suction traps can be used to make, adjust and improve predictions of their abundance so that the risk of plant viruses spreading through aphids can be more accurately predicted. With the increasing demand for alternatives to conventional pesticide use in crop protection, the need for predictive models is growing, e.g. as a basis for resistance development and as a measure for resistance management. In this context, advancing climate change has a strong influence on the total abundance of migrating aphids as well as on the peak occurrences of aphids within a year. Using aphids as a model organism, we demonstrate the possibilities of systematic monitoring of insect pests and the potential of future technical developments in the subsequent automated identification of individuals through to the use of case data for intelligent forecasting models. Using aphids as an example, we show the potential for systematic monitoring of insect pests through technical developments in the automated identification of individuals from static images (i.e. advances in image recognition software). We discuss the potential applications with regard to the automatic processing of insect case data and the development of intelligent prediction models.
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Affiliation(s)
- Philipp Batz
- ALM – Adaptiv Lernende Maschinen – Gesellschaft mit beschränkter Haftung (GmbH), Nisterau, Germany
| | - Torsten Will
- Institute for Resistance Research and Stress Tolerance, Julius Kühn-Institute, Federal Research Centre for Cultivated Plants, Quedlinburg, Germany
| | - Sebastian Thiel
- ALM – Adaptiv Lernende Maschinen – Gesellschaft mit beschränkter Haftung (GmbH), Nisterau, Germany
| | - Tim Mark Ziesche
- Institute for Resistance Research and Stress Tolerance, Julius Kühn-Institute, Federal Research Centre for Cultivated Plants, Quedlinburg, Germany
| | - Christoph Joachim
- Institute for Plant Protection in Field Crops and Grassland, Julius Kühn-Institute, Federal Research Centre for Cultivated Plants, Braunschweig, Germany
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20
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Wang J, Mao Y, Gao X, Zhang Y. Recurrence risk stratification for locally advanced cervical cancer using multi-modality transformer network. Front Oncol 2023; 13:1100087. [PMID: 36874136 PMCID: PMC9978213 DOI: 10.3389/fonc.2023.1100087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/01/2023] [Indexed: 02/18/2023] Open
Abstract
Objectives Recurrence risk evaluation is clinically significant for patients with locally advanced cervical cancer (LACC). We investigated the ability of transformer network in recurrence risk stratification of LACC based on computed tomography (CT) and magnetic resonance (MR) images. Methods A total of 104 patients with pathologically diagnosed LACC between July 2017 and December 2021 were enrolled in this study. All patients underwent CT and MR scanning, and their recurrence status was identified by the biopsy. We randomly divided patients into training cohort (48 cases, non-recurrence: recurrence = 37: 11), validation cohort (21 cases, non-recurrence: recurrence = 16: 5), and testing cohort (35 cases, non-recurrence: recurrence = 27: 8), upon which we extracted 1989, 882 and 315 patches for model's development, validation and evaluation, respectively. The transformer network consisted of three modality fusion modules to extract multi-modality and multi-scale information, and a fully-connected module to perform recurrence risk prediction. The model's prediction performance was assessed by six metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, f1-score, sensitivity, specificity and precision. Univariate analysis with F-test and T-test were conducted for statistical analysis. Results The proposed transformer network is superior to conventional radiomics methods and other deep learning networks in both training, validation and testing cohorts. Particularly, in testing cohort, the transformer network achieved the highest AUC of 0.819 ± 0.038, while four conventional radiomics methods and two deep learning networks got the AUCs of 0.680 ± 0.050, 0.720 ± 0.068, 0.777 ± 0.048, 0.691 ± 0.103, 0.743 ± 0.022 and 0.733 ± 0.027, respectively. Conclusions The multi-modality transformer network showed promising performance in recurrence risk stratification of LACC and may be used as an effective tool to help clinicians make clinical decisions.
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Affiliation(s)
- Jian Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Yixiao Mao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
| | - Xinna Gao
- Department of Radiation Oncology, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Yu Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.,Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China
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21
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Kophamel S, Ward LC, Konovalov DA, Mendez D, Ariel E, Cassidy N, Bell I, Balastegui Martínez MT, Munns SL. Field‐based adipose tissue quantification in sea turtles using bioelectrical impedance spectroscopy validated with CT scans and deep learning. Ecol Evol 2022; 12:e9610. [PMCID: PMC9748411 DOI: 10.1002/ece3.9610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 11/23/2022] [Indexed: 12/15/2022] Open
Affiliation(s)
- Sara Kophamel
- College of Public Health, Medical and Veterinary Sciences James Cook University Townsville Queensland Australia
| | - Leigh C. Ward
- School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia
| | - Dmitry A. Konovalov
- College of Science and Engineering James Cook University Townsville Queensland Australia
| | - Diana Mendez
- Australian Institute of Tropical Health and Medicine Townsville Queensland Australia
| | - Ellen Ariel
- College of Public Health, Medical and Veterinary Sciences James Cook University Townsville Queensland Australia
| | - Nathan Cassidy
- North Queensland X‐Ray Services Townsville Queensland Australia
| | - Ian Bell
- Department of Environment and Science Queensland Government Townsville Queensland Australia
| | | | - Suzanne L. Munns
- College of Public Health, Medical and Veterinary Sciences James Cook University Townsville Queensland Australia
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22
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Sanchez T, Bray EM, Jobic P, Guez J, Letournel AC, Charpiat G, Cury J, Jay F. dnadna: a deep learning framework for population genetics inference. Bioinformatics 2022; 39:6851140. [PMID: 36445000 PMCID: PMC9825738 DOI: 10.1093/bioinformatics/btac765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 10/30/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022] Open
Abstract
MOTIVATION We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination and re-usability of neural networks designed for population genetic data. RESULTS dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna utility functions, training procedure and test environment, which saves time and decreases the likelihood of bugs. Second, the implemented networks can be re-optimized based on user-specified training sets and/or tasks. Newly implemented architectures and pre-trained networks are easily shareable with the community for further benchmarking or other applications. Finally, users can apply pre-trained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without requiring extensive knowledge in deep learning or coding in general. dnadna comes with a peer-reviewed, exchangeable neural network, allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to community contributions. AVAILABILITY AND IMPLEMENTATION dnadna is a Python (≥3.7) package, its repository is available at gitlab.com/mlgenetics/dnadna and its associated documentation at mlgenetics.gitlab.io/dnadna/.
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Affiliation(s)
| | | | - Pierre Jobic
- Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France
- ENS Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Jérémy Guez
- Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France
- UMR7206 Eco-Anthropologie, Muséum National d’Histoire Naturelle, CNRS, Université de Paris, 75016 Paris, France
| | - Anne-Catherine Letournel
- Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France
| | - Guillaume Charpiat
- Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France
| | - Jean Cury
- To whom correspondence should be addressed. or
| | - Flora Jay
- To whom correspondence should be addressed. or
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23
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Abstract
AbstractRapid advances in hardware and software, accompanied by public- and private-sector investment, have led to a new generation of data-driven computational tools. Recently, there has been a particular focus on deep learning—a class of machine learning algorithms that uses deep neural networks to identify patterns in large and heterogeneous datasets. These developments have been accompanied by both hype and scepticism by ecologists and others. This review describes the context in which deep learning methods have emerged, the deep learning methods most relevant to ecosystem ecologists, and some of the problem domains they have been applied to. Deep learning methods have high predictive performance in a range of ecological contexts, leveraging the large data resources now available. Furthermore, deep learning tools offer ecosystem ecologists new ways to learn about ecosystem dynamics. In particular, recent advances in interpretable machine learning and in developing hybrid approaches combining deep learning and mechanistic models provide a bridge between pure prediction and causal explanation. We conclude by looking at the opportunities that deep learning tools offer ecosystem ecologists and assess the challenges in interpretability that deep learning applications pose.
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