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.

The effects of light pollution on migratory animal behavior.

Light pollution is a global threat to biodiversity, especially migratory organisms, some of which traverse hemispheric scales. Research on light pollution has grown significantly over the past decades, but our review of migratory organisms demonstrates gaps in our understanding, particularly beyond migratory birds. Research across spatial scales reveals the multifaceted effects of artificial light on migratory species, ranging from local and regional to macroscale impacts.

Application of Deep Learning to Community-Science-Based Mosquito Monitoring and Detection of Novel Species.

Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.

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

We point out complications inherent in biodiversity inventory metrics when applied to large-scale datasets. The number of units of inventory effort (e.g.

Deep Learning Algorithms Improve Automated Identification of Chagas Disease Vectors.

Vector-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.

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 , 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.