Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors

This is a pre-copyedited, author-produced version of an article accepted for publication in Journal of Medical Entomology following peer review. The version of record is available online at: https;//doi.org/https://doi.org/10.1093/jme/tjz065Vector-borne Chagas disease is endemic to the Americas and imposes significant economic and social burdens on public health. In a previous contribution, we presented an automated identification system that was able to discriminate among 12 Mexican and 39 Brazilian triatomine (Hemiptera: Reduviidae) species from digital images. To explore the same data more deeply using machine-learning approaches, hoping for improvements in classification, we employed TensorFlow, an open-source software platform for a deep learning algorithm. We trained the algorithm based on 405 images for Mexican triatomine species and 1,584 images for Brazilian triatomine species. Our system achieved 83.0 and 86.7% correct identification rates across all Mexican and Brazilian species, respectively, an improvement over comparable rates from statistical classifiers (80.3 and 83.9%, respectively). Incorporating distributional information to reduce numbers of species in analyses improved identification rates to 95.8% for Mexican species and 98.9% for Brazilian species. Given the ‘taxonomic impediment’ and difficulties in providing entomological expertise necessary to control such diseases, automating the identification process offers a potential partial solution to crucial challenges

Automated identification of insect vectors of Chagas disease in Brazil and Mexico: the Virtual Vector Lab

Identification of arthropods important in disease transmission is a crucial, yet difficult, task that can demand considerable training and experience. An important case in point is that of the 150+ species of Triatominae, vectors of Trypanosoma cruzi, causative agent of Chagas disease across the Americas. We present a fully automated system that is able to identify triatomine bugs from Mexico and Brazil with an accuracy consistently above 80%, and with considerable potential for further improvement. The system processes digital photographs from a photo apparatus into landmarks, and uses ratios of measurements among those landmarks, as well as (in a preliminary exploration) two measurements that approximate aspects of coloration, as the basis for classification. This project has thus produced a working prototype that achieves reasonably robust correct identification rates, although many more developments can and will be added, and—more broadly—the project illustrates the value of multidisciplinary collaborations in resolving difficult and complex challenges

Inequalities in noise will affect urban wildlife

Understanding how systemic biases influence local ecological communities is essential for developing just and equitable environmental practices that prioritize both human and wildlife well-being. With over 270 million residents inhabiting urban areas in the United States, the socioecological consequences of racially targeted zoning, such as redlining, need to be considered in urban planning. There is a growing body of literature documenting the relationships between redlining and the inequitable distribution of environmental harms and goods, green space cover and pollutant exposure. However, it remains unknown whether historical redlining affects the distribution of urban noise or whether inequitable noise drives an ecological change in urban environments. Here we conducted a spatial analysis of how urban noise corresponds to the distribution of redlining categories and a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence to indicate that noise is inequitably distributed in redlined urban communities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife, reinforcing the interrelatedness of socioecological outcomes. These findings lay a foundation for future research that advances relationships between acoustic and urban ecology through centring equity and challenging systems of oppression in wildlife studies

Triatomine bug pictures from “Cellphone picture-based, genus-level automated identification of Chagas disease vectors: effects of picture orientation on the performance of five machine-learning algorithms"

Triatomine bug pictures from "Cellphone picture-based, genus-level automated identification of Chagas disease vectors: effects of picture orientation on the performance of five machine-learning algorithms" (under review, Ecological Informatics, Elsevier; print ISSN: 1574-9541; online ISSN: 1878-0512). The access link will be available on the article web page upon publication.</p

NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions. Here, we present NABat ML, an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and trained it on \u3e600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future ‘unseen’ data. We evaluated model performance using a comprehensive, independent, holdout dataset. NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted-average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud-environment computing instance, the entire model training process took \u3c16 h. Synthesis and applications. Our convolutional neural network (CNN)-based model, NABat ML, classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species-level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open-source and reproducible, enabling future implementations as software on end-user devices and cloud-based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big-data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad-scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species