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

Inventory statistics meet big data: complications for estimating numbers of species

This work is licensed under a Creative Commons Attribution 4.0 International License.We point out complications inherent in biodiversity inventory metrics when applied to large-scale datasets. The number of units of inventory effort (e.g., days of inventory effort) in which a species is detected saturates, such that crucial numbers of detections of rare species approach zero. Any rare errors can then come to dominate species richness estimates, creating upward biases in estimates of species numbers. We document the problem via simulations of sampling from virtual biotas, illustrate its potential using a large empirical dataset (bird records from Cape May, NJ, USA), and outline the circumstances under which these problems may be expected to emerge

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 the extent to which systemic biases influence local ecological communities is essential for developing just and equitable environmental practices. With over 270 million people across the United States living in urban areas, understanding the socio-ecological consequences of racially-targeted zoning, such as redlining, provides crucial information for urban planning. There is a growing body of literature documenting the relationships between redlining and disparities in the distribution of environmental harms and goods, including inequities in green space cover and pollutant exposure. Yet, it remains unknown whether noise pollution is also inequitably distributed, and whether inequitable noise is an important driver of ecological change in urban environments. We conducted 1) a spatial analysis of urban noise to determine the extent to which noise overlaps with the distribution of redlining categories and 2) a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence that noise is inequitably distributed in cities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife. These findings lay a foundation for future research that advances acoustic and urban ecology by centering equity and challenging systems of oppression.Data can be viewed using any software that can open a .csv file. Code can be viewed using any software that can open a .txt file.Funding provided by: Colorado State UniversityCrossref Funder Registry ID: http://dx.doi.org/10.13039/100007235Award Number:Spatial Analysis of Urban Noise Pollution To evaluate noise exposure across HOLC redlining grades for 83 U.S. cities in our study, we acquired spatial data on the distribution of HOLC grades across U.S. cities from the Mapping Inequality Project. We also acquired data on road, rail, and aircraft noise (hereafter transportation noise models), from the U.S. Department of Transportation, National Transportation Noise Map 2018. The transportation noise models represent potential exposure to transportation noise reported on a decibel scale in a 30m x 30m pixel resolution. Here noise represents the average noise energy produced by road, rail, and aviation networks over a 24-hour period, measured in A-weighted decibels (dBA) (LAeq, 24h) at sampling locations deployed across a uniform grid in each city at an elevation of 1.5 m above ground level. Noise levels below 35 dBA are assumed to have minimal negative impacts to humans and the environment and thus are represented with null values in the transportation noise models. For each HOLC grade and each city, we used zonal statistics in ArcGIS Desktop v. 10.7 to calculate descriptive statistics (median, minimum, maximum, area) for the 30m x 30m pixels in the transportation noise models with non-null noise values (i.e., values > 35 dBA). We used the resulting zonal statistics estimates to calculate an area-corrected measure of excess noise: N = (r * Md)/a where N is excess noise in each HOLC grade (with units of dBA/900 m2); r is the area covered by the 30m x 30m pixels with noise values >35 dBA across all polygons of the same HOLC grade in each city; Md is the median transportation noise value (in dBA) for those same pixels; and a is the total area of all polygons of the same HOLC grade in each city. Literature Review Methodology To assess the effects of noise on wildlife in urban environments, we conducted a literature review using Thompson's ISI Web of Science and adapting the methods of Shannon et al. (2016). We adjusted Shannon et al. (2016) search criteria to include urban phrases, resulting in the following search terms (TS=(WILDLIFE OR ANIMAL OR MAMMAL OR REPTILE OR AMPHIBIAN OR BIRD OR FISH OR INVERTEBRATE) AND TS=(NOISE OR SONAR) AND TS=(CITY OR *URBAN OR METROPOLITAN)). We only selected papers published between 1990 and 23 June 2021 (i.e., the date we conducted our search) within the ISI Web of Science categories of 'Acoustics', 'Zoology', 'Ecology', 'Environmental Sciences', 'Ornithology', 'Biodiversity Conservation', 'Evolutionary Biology', and 'Marine Freshwater Biology'. This returned 691 peer-reviewed papers, which we filtered so only empirical studies focused on documenting the effects of anthropogenic noise on wildlife in urban or suburban ecosystems or the effects of urban noise on wildlife in rural environments were included in the final data set. We excluded reviews, meta-analyses, methods papers, and research that took place outside of urban or suburban areas where the noise was not explicitly denoted as urban (e.g., omitted studies that measured traffic noise by parks and reserves in rural areas). For the 241 articles previously analyzed in Shannon et al. (2016), one of our authors reviewed each paper to determine which studies were focused on urban noise. We also verified the noise levels that caused a significant biological response, noting each noise level if multiple responses were recorded. For any new articles published since the Shannon et al. (2016) dataset or those published between 1990 and 2013 but not reviewed by Shannon et al. (2016) (n = 96), two of our authors reviewed each paper to first determine which studies met our criteria and then compiled data on a number of variables of interest, including the noise levels and their resulting biological responses that were statistically significant. For this subset of papers, one author was randomly assigned a list of papers and then a second author was randomly assigned to assess the accuracy of the data collected by the first author. Any discrepancies were discussed as a group until an agreement was reached. Noise categories (environmental, transportation, industrial, multiple, other) were chosen for each paper by noting the explicitly stated source or description of urban noise described in the methodology. Noise levels and their units were reported for each paper, with only noise levels reported in decibels (dB) being used in data analysis. We recorded the sound metric used (i.e., SPL, SPL Max, Leq) for each paper and also recorded the weightings for each noise level

Inequalities in noise will affect urban wildlife

Understanding the extent to which systemic biases influence local ecological communities is essential for developing just and equitable environmental practices. With over 270 million people across the United States living in urban areas, understanding the socio-ecological consequences of racially-targeted zoning, such as redlining, provides crucial information for urban planning. There is a growing body of literature documenting the relationships between redlining and disparities in the distribution of environmental harms and goods, including inequities in green space cover and pollutant exposure. Yet, it remains unknown whether noise pollution is also inequitably distributed, and whether inequitable noise is an important driver of ecological change in urban environments. We conducted 1) a spatial analysis of urban noise to determine the extent to which noise overlaps with the distribution of redlining categories and 2) a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence that noise is inequitably distributed in cities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife. These findings lay a foundation for future research that advances acoustic and urban ecology by centering equity and challenging systems of oppression.Data can be viewed using any software that can open a .csv file. Code can be viewed using any software that can open a .txt file.Funding provided by: Colorado State UniversityCrossref Funder Registry ID: http://dx.doi.org/10.13039/100007235Award Number:Spatial Analysis of Urban Noise Pollution To evaluate noise exposure across HOLC redlining grades for 83 U.S. cities in our study, we acquired spatial data on the distribution of HOLC grades across U.S. cities from the Mapping Inequality Project. We also acquired data on road, rail, and aircraft noise (hereafter transportation noise models), from the U.S. Department of Transportation, National Transportation Noise Map 2018. The transportation noise models represent potential exposure to transportation noise reported on a decibel scale in a 30m x 30m pixel resolution. Here noise represents the average noise energy produced by road, rail, and aviation networks over a 24-hour period, measured in A-weighted decibels (dBA) (LAeq, 24h) at sampling locations deployed across a uniform grid in each city at an elevation of 1.5 m above ground level. Noise levels below 35 dBA are assumed to have minimal negative impacts to humans and the environment and thus are represented with null values in the transportation noise models. For each HOLC grade and each city, we used zonal statistics in ArcGIS Desktop v. 10.7 to calculate descriptive statistics (median, minimum, maximum, area) for the 30m x 30m pixels in the transportation noise models with non-null noise values (i.e., values > 35 dBA). We used the resulting zonal statistics estimates to calculate an area-corrected measure of excess noise: N = (r * Md)/a where N is excess noise in each HOLC grade (with units of dBA/900 m2); r is the area covered by the 30m x 30m pixels with noise values >35 dBA across all polygons of the same HOLC grade in each city; Md is the median transportation noise value (in dBA) for those same pixels; and a is the total area of all polygons of the same HOLC grade in each city. Literature Review Methodology To assess the effects of noise on wildlife in urban environments, we conducted a literature review using Thompson's ISI Web of Science and adapting the methods of Shannon et al. (2016). We adjusted Shannon et al. (2016) search criteria to include urban phrases, resulting in the following search terms (TS=(WILDLIFE OR ANIMAL OR MAMMAL OR REPTILE OR AMPHIBIAN OR BIRD OR FISH OR INVERTEBRATE) AND TS=(NOISE OR SONAR) AND TS=(CITY OR *URBAN OR METROPOLITAN)). We only selected papers published between 1990 and 23 June 2021 (i.e., the date we conducted our search) within the ISI Web of Science categories of 'Acoustics', 'Zoology', 'Ecology', 'Environmental Sciences', 'Ornithology', 'Biodiversity Conservation', 'Evolutionary Biology', and 'Marine Freshwater Biology'. This returned 691 peer-reviewed papers, which we filtered so only empirical studies focused on documenting the effects of anthropogenic noise on wildlife in urban or suburban ecosystems or the effects of urban noise on wildlife in rural environments were included in the final data set. We excluded reviews, meta-analyses, methods papers, and research that took place outside of urban or suburban areas where the noise was not explicitly denoted as urban (e.g., omitted studies that measured traffic noise by parks and reserves in rural areas). For the 241 articles previously analyzed in Shannon et al. (2016), one of our authors reviewed each paper to determine which studies were focused on urban noise. We also verified the noise levels that caused a significant biological response, noting each noise level if multiple responses were recorded. For any new articles published since the Shannon et al. (2016) dataset or those published between 1990 and 2013 but not reviewed by Shannon et al. (2016) (n = 96), two of our authors reviewed each paper to first determine which studies met our criteria and then compiled data on a number of variables of interest, including the noise levels and their resulting biological responses that were statistically significant. For this subset of papers, one author was randomly assigned a list of papers and then a second author was randomly assigned to assess the accuracy of the data collected by the first author. Any discrepancies were discussed as a group until an agreement was reached. Noise categories (environmental, transportation, industrial, multiple, other) were chosen for each paper by noting the explicitly stated source or description of urban noise described in the methodology. Noise levels and their units were reported for each paper, with only noise levels reported in decibels (dB) being used in data analysis. We recorded the sound metric used (i.e., SPL, SPL Max, Leq) for each paper and also recorded the weightings for each noise level

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