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There are various color correction techniques that can be applied to digital photographs to account for environmental lighting variations. This manuscript contains a proposed method for such color correction. The method involves saturating an image by a specified percentage of its pixels via upper and lower percentage histogram manipulation using the image’s RGB histograms. Variations of this new technique, the white balance (WB) correction method, and a multivariable fit are used to test its performance against common color correction techniques. The findings demonstrate that the upper and lower percentage histogram manipulation method is not only more applicable to photos because it doesn’t require calibration regions to be sampled but it is also more consistent in its correction of photos when there are substantial gray scale features (e.g. a black and white grid or text). Our motivation for testing these techniques is to find the most robust color correction technique that is broadly applicable (not requiring a color checker chart) and is consistent across different lighting. KEYWORDS: Color Correction; Histogram Manipulation; Saturation; White Balance; Scientific Image Analysis; Color Comparisons; Euclidean Distance; Standard Deviation; Color Difference 

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. 

One of the biggest challenges with species conservation is collecting accurate and efficient information on population sizes, especially from species that are difficult to count. Bats worldwide are declining due to disease, habitat destruction, and climate change, and many species lack reliable population information to guide management decisions. Current approaches for estimating population sizes of bats in densely occupied colonies are time-intensive, may negatively impact the population due to disturbance, and/or have low accuracy. Research-based video tracking options are rarely used by conservation or management agencies for animal counting due to the perceived training and elevated operating costs. In this paper, we present BatCount, a free software program created in direct consultation with end-users designed to automatically count bats emerging from cave roosts (historical populations 20,000–250,000) with a streamlined and user-friendly interface. We report on the software package and provide performance metrics for different recording habitat conditions. Our analysis demonstrates that BatCount is an efficient and reliable option for counting bats in flight, with performance hundreds of times faster than manual counting, and has important implications for range- and species-wide population monitoring. Furthermore, this software can be extended to count any organisms moving across a camera including birds, mammals, fish or insects. 

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 

Bird bills possess an important thermoregulatory function as they are a site for environmental heat exchange. Previous studies have demonstrated that birds in warmer climates have larger bills than those living in colder climates, as larger bills can dissipate more heat. Because this dry heat transfer does not incur water loss, it may be additionally advantageous in water‐restricted habitats. Here, we examine the influence of climate on bill morphology inToxostomathrashers, a group of 10 North American species that varied in bill morphology and occupied climate niche, with several species inhabiting arid climates. Past examinations of thrasher bill morphology have only considered foraging, leaving unanswered the role of climate in morphological divergence within this group. We photographed 476Toxostomamuseum specimens encompassing all 10 species and calculated bill measurements from the photos using a MATLAB‐based program. For each species, we calculated occupied climate niche using data from WorldClim describing temperature and precipitation. We found no reliable significant relationships between climate variables and bill morphology across species, suggesting that other factors such as foraging behavior may be more important in shaping bill morphology in this genus. Within species, we found threeToxostomaspecies have significant relationships between bill morphology and climate that follow Allen's rule. However, we also found the relationships between climate and bill morphology varied in strength and direction across species. Notably, we found a negative relationship between maximum temperature of the hottest month and bill surface area in LeConte's thrasher, which occupies the hottest and most arid climates of the thrashers. This adds to the evidence that Allen's rule may reverse in extremely hot climates when the bill may become a heat sink instead of a heat radiator. These results demonstrate the importance of considering the generality of ecogeographical rules across lineages that occupy extreme climates.