Diversity and Evolution of Frog Visual Opsins: Spectral Tuning and Adaptation to Distinct Light Environments

Visual systems adapt to different light environments through several avenues including optical changes to the eye and neurological changes in how light signals are processed and interpreted. Spectral sensitivity can evolve via changes to visual pigments housed in the retinal photoreceptors through gene duplication and loss, differential and coexpression, and sequence evolution. Frogs provide an excellent, yet understudied, system for visual evolution research due to their diversity of ecologies (including biphasic aquatic-terrestrial life cycles) that we hypothesize imposed different selective pressures leading to adaptive evolution of the visual system, notably the opsins that encode the protein component of the visual pigments responsible for the first step in visual perception. Here, we analyze the diversity and evolution of visual opsin genes from 93 new eye transcriptomes plus published data for a combined dataset spanning 122 frog species and 34 families. We find that most species express the four visual opsins previously identified in frogs but show evidence for gene loss in two lineages. Further, we present evidence of positive selection in three opsins and shifts in selective pressures associated with differences in habitat and life history, but not activity pattern. We identify substantial novel variation in the visual opsins and, using microspectrophotometry, find highly variable spectral sensitivities, expanding known ranges for all frog visual pigments. Mutations at spectral-tuning sites only partially account for this variation, suggesting that frogs have used tuning pathways that are unique among vertebrates. These results support the hypothesis of adaptive evolution in photoreceptor physiology across the frog tree of life in response to varying environmental and ecological factors and further our growing understanding of vertebrate visual evolution.

Automatic ladybird beetle detection using deep-learning models

Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system. First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image. Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output. This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project. The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks. These successful results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.