Random forest is the best species predictor for a community of insectivorous bats inhabiting a mountain ecosystem of central Mexico

Bionic study of distance-azimuth discrimination of multi-scattered point objects in bat bio-sonar

This paper presents a novel approach to enhance the discrimination capacity of multi-scattered point objects in bat bio-sonar. A broadband interferometer mathematical model is developed, incorporating both distance and azimuth information, to simulate the transmitted and received signals of bats. The Fourier transform is employed to simulate the preprocessing step of bat information for feature extraction. Furthermore, the bat bio-sonar model based on convolutional neural network (BS-CNN) is constructed to compensate for the limitations of conventional machine learning and CNN networks, including three strategies: Mix-up data enhancement, joint feature and hybrid atrous convolution module. The proposed BS-CNN model emulates the perceptual nerves of the bat brain for distance-azimuth discrimination and compares with four conventional classifiers to assess its discrimination efficacy. Experimental results demonstrate that the overall discrimination accuracy of the BS-CNN model is 93.4%, surpassing conventional CNN networks and machine learning methods by at least 5.9%. This improvement validates the efficacy of the BS-CNN bionic model in enhancing the discrimination accuracy in bat bio-sonar and offers valuable references for radar and sonar target classification.

Weather, ultrasonic, cranial and body traits predict insect diet hardness in a Central Mexican bat community

Insectivorous bats exhibit food preferences for specific attributes in their prey. Hardness has been defined as an important prey attribute, and in some cases a limiting factor in foraging decisions for smaller compared to larger bat species. The goal of this study was to identify which factors influence the selection of prey hardness in a vespertilionid bat community. We investigated food consumed by bats by analyzing fecal samples obtained from eight bat species coexisting in a mountain ecosystem of central Mexico and correlate non-phylogenetically and phylogenetically prey hardness to weather, bat´s body, cranial and ultrasonic call structure variables. Results showed that diet of vespertilionid bats was mainly represented by Diptera, Neuroptera, Lepidoptera and Coleoptera consumption. The qualitative prey hardness index (From soft 1 to hard 5) ranked bats as: Myotis melanorhinus, Corynorhinus mexicanus, Myotis volans, Myotis californicus (< 3); Myotis velifer (< 4); Eptesicus fuscus, Idionycteris phyllotis and Myotis thysanodes (> 4.2). Prey hardness was positively correlated to minimum and mean temperatures, bat´s body weight, total and forearm lengths, cranial variables as: zygomatic breadth, mandibular length, height of the coronoid process, lower molar width, C-M3 superior and inferior rows length and upper molar width; and negatively to ultrasonic variables as total slope, call duration, low and high frequencies, band width and frequency maximum power. Considering phylogenies, prey hardness positively correlated to mandibular length, C-M3 inferior and superior rows lengths (p < 0.05). Our results showed that environmental, morphological and echolocation variables can be used as predictors of preferred insect prey in a community of vespertilionid bats.