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Accurate population counts are essential for understanding the status of species and for researchers studying various phenomena including monitoring the relationship between environmental stresses and the spread of disease within populations. Both small roosts and large colonies of bats provide challenges when attempting to determine an accurate population count. Recently, there have been a number of new video analysis software applications, that are available on the internet, which can be used to provide population counts. When software-based counts are compared with manual counts, the software provides counts that are substantially less labor intensive, determined substantially more quickly, and have the potential to be more accurate. This short paper discusses the use of neural networks to determine the number of bats that there are in a region when multiple bats may overlap. The work discussed in this manuscript demonstrates that the counts of multiple overlapping bats can be improved using trained neural networks. This is a critical improvement for providing accurate counts in high density videos. This manuscript contains the biological motivations, and a brief overview of how artificial intelligence is being implemented. The results discussed compare the accuracy values of neural networks for a few case studies including cross-comparisons of data trained on different video types and for different animals which can have accuracy values above 90 % for comparable video types. Finally, the generation and use of synthetic images, to increase the amount of data in a training set, is also discussed, which resulted in a trained neural network that produced an accuracy value of 80% on 12 unbiased categories. 

Phenotypic analysis from digital photographs is a useful tool in bioinformatics, and it has become increasingly important in the study of museum specimens as more natural history museum archives are digitized. However, steep learning curves and high costs associated with currently available image analysis software limits archive use by undergraduates, K-12 students, researchers at smaller educational institutions, and citizen scientists. We have created the Scientific Image Analysis (SIA) application to overcome these limitations with software that is freely available to any user and has an intuitive interface. SIA includes tools to measure length, angle, color and area from digital photographs, and includes tools to correct for color biases and skew from the perspective of the photograph. In this short paper we test these tools and their repeatability by measuring 497 avian museum specimens. We have quantified variation in bill length, angle of curvature of the bill, and plumage color. We find that measurements from SIA tools were highly repeatable across measurers, across replicate photographs of the same specimen, and were robust to user choices within SIA tools. Index Terms—bioinformatics, color correction, graphical output, image analysis, morphometrics, museum specimens