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Simple Summary: Chagas disease is endemic to the state of Texas in the United States but does not have consistent surveillance or reporting. We utilized multiple sampling sites and different species of triatomine to gain data on the blood meal sources found by DNA testing for the host and vector species identities. From domestic, peridomestic, and rural sites, we found a breadth of blood meal origins including mammals, chickens, and reptiles. Unique non-native taxa utilized for blood meals enabled us to also report on extensive foraging distances for the vectors. Understanding the diversity of blood meal sources and the distances the vectors travel between meals and daytime refuges are both important aspects for understanding the spread of this disease. The prevalence of Trypanosoma cruzi was assessed in 117 triatomine insects from central Texas. The qPCR-based results revealed T. cruzi in 59% of the insects (62 adults and eight nymphs), with overall prevalences of T. cruzi of 0% (0/9), 64% (11/17), 58% (10/17), 73% (30/41), and 57% (19/33) for the Bastrop, Caldwell, Gonzales, Guadalupe, and Hays counties, respectively. Analyses of 18S rRNA fragments confirmed T. cuzi in 81% of these samples. Vectors were identified as Triatoma gerstaeckeri (35% of which 65% were positive for T. cruzi), T. sanguisuga (21%, 43% positive), and Paratriatoma leticularia (0.3%, 100% positive). Food sources were recovered from 29% of the insects. Raccoons were 53% of the blood meals (83% positive for T. cruzi), while the remainder came from a variety of sources, including humans (33% positive), house geckos, Eastern woodrats, plain-bellied water snakes (50% positive), hispid cotton rats (0% positive), chickens (100% positive); Asian forest turtles, bison, and pigs (0% positive). The serendipitous detection of blood meal sources at known minimum distances from the collection of the vector insect enabled us to provide several instances where the insect foraging distance was greater than 400 m. These vector foraging distances are novel information that can assist in our understanding of the landscape dynamics for the spread of the pathogen. [ABSTRACT FROM AUTHOR]

The molecular detection of DNA fragments that are shed into the environment (eDNA) has become an increasingly applied tool used to inventory biological communities and to perform targeted species surveys. This method is particularly useful in habitats where it is difficult or not practical to visually detect or trap the target organisms. Central Texas Eurycea salamanders inhabit both surface and subterranean aquatic environments. Subterranean surveys are challenging or infeasible, and the detection of salamander eDNA in water samples is an appealing survey technique for these situations. Here, we develop and validate an eDNA assay using quantitative PCR for E. chisholmensis, E. naufragia, and E. tonkawae. These three species are federally threatened and constitute the Septentriomolge clade that occurs in the northern segment of the Edwards Aquifer. First, we validated the specificity of the assay in silico and with DNA extracted from tissue samples of both target Septentriomolge and non-target amphibians that overlap in distribution. Then, we evaluated the sensitivity of the assay in two controls, one with salamander-positive water and one at field sites known to be occupied by Septentriomolge. For the salamander-positive control, the estimated probability of eDNA occurrence (ψ) was 0.981 (SE = 0.019), and the estimated probability of detecting eDNA in a qPCR replicate (p) was 0.981 (SE = 0.011). For the field control, the estimated probability of eDNA occurring at a site (ψ) was 0.938 (95% CRI: 0.714–0.998). The estimated probability of collecting eDNA in a water sample (θ) was positively correlated with salamander relative density and ranged from 0.371 (95% CRI: 0.201–0.561) to 0.999 (95% CRI: 0.850– > 0.999) among sampled sites. Therefore, sites with low salamander density require more water samples for eDNA evaluation, and we determined that our site with the lowest estimated θ would require seven water samples for the cumulative collection probability to exceed 0.95. The estimated probability of detecting eDNA in a qPCR replicate (p) was 0.882 (95% CRI: 0.807–0.936), and our assay required two qPCR replicates for the cumulative detection probability to exceed 0.95. In complementary visual encounter surveys, the estimated probability of salamanders occurring at a known-occupied site was 0.905 (SE = 0.096), and the estimated probability of detecting salamanders in a visual encounter survey was 0.925 (SE = 0.052). We additionally discuss future research needed to refine this method and understand its limitations before practical application and incorporation into formal survey protocols for these taxa. [ABSTRACT FROM AUTHOR]

Simple Summary: The disappearance of many reptiles and amphibian species is directly and indirectly connected with habitat modification, introducing invasive species, disease, pollution, and climate change. Monitoring endangered species is essential for conservation action to mitigate the predominant threats to endangered species. According to wildlife researchers, visual information provides definitive evidence of an animal's distribution patterns and activity within an environmental context. At the Department of Biology and The Ingram School of Engineering at Texas State University and Texas A&M University, we collaborated on a camera trap project to monitor species from images in Texas, USA. We developed a framework to analyze the acquired images using artificial intelligence-driven technology and computer vision methods. The project aims to classify three broad groups of herpetofaunal species (i.e., toads/frogs, lizards, and snakes) from camera trap images with deep learning architectures. The experiment includes balancing the imbalanced dataset, investigating several image preprocessing techniques, and augmentation procedures. The classification results present tremendous prospects for automated species identification from challenging biological image data. The research outcome is essential to the academic community and wildlife researchers for conservation and monitoring purpose. Accurate identification of animal species is necessary to understand biodiversity richness, monitor endangered species, and study the impact of climate change on species distribution within a specific region. Camera traps represent a passive monitoring technique that generates millions of ecological images. The vast numbers of images drive automated ecological analysis as essential, given that manual assessment of large datasets is laborious, time-consuming, and expensive. Deep learning networks have been advanced in the last few years to solve object and species identification tasks in the computer vision domain, providing state-of-the-art results. In our work, we trained and tested machine learning models to classify three animal groups (snakes, lizards, and toads) from camera trap images. We experimented with two pretrained models, VGG16 and ResNet50, and a self-trained convolutional neural network (CNN-1) with varying CNN layers and augmentation parameters. For multiclassification, CNN-1 achieved 72% accuracy, whereas VGG16 reached 87%, and ResNet50 attained 86% accuracy. These results demonstrate that the transfer learning approach outperforms the self-trained model performance. The models showed promising results in identifying species, especially those with challenging body sizes and vegetation. [ABSTRACT FROM AUTHOR]