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Most animals face changes in the availability of food and the environmental conditions in the places where they live. In response, they need to adjust their behavioral, physiological, and morphological traits. In temperate zones and high latitudes, bats increase their body mass (Mb) in autumn to store fat reserves and use them during hibernation. However, other small mammals decrease their Mb prior to winter to reduce the energetic requirements of individuals. These changes are unknown for bats inhabiting other highly energetic demanding environments. We measured changes in Mb of 84 non-reproductive males of Eptesicus fuscus inhabiting a tropical montane ecosystem in central Mexico over seasons. We also examined the relationship of bats' Mb with the minimum ambient temperature (Ta, °C) and mean precipitation (mm). Bats presented an increase in Mb from March to June, followed by a decrease from September to November and presented the lowest Mb from November to March, in the dry-cold season. The results suggest that the pattern of changes in Mb could be the result of two non-exclusive components related to the bats' energy budget, the energetic demands experienced by the bats throughout the year and the morphological adaptations animals could display to reduce their energy requirements during the winter. [ABSTRACT FROM AUTHOR]

Bats are nocturnal animals that can be identified by recording and analysing quantitatively their echolocation calls. For this task, many studies have used both parametric and non-parametric approximations with a variety of results. This urges the necessity of developing more call libraries, that should be analysed using the different statistical approaches to test their performance. This could be relevant in countries holding high biodiversity where the knowledge of the variation in the call structure among species is still scarce. We constructed and validated a call library from bats inhabiting a mountain ecosystem of central Mexico using the Linear Discriminant Function, Artificial Neural Network and Random Forest approaches. We recorded and analysed 2,325 pulses from 114 individuals and 16 bat species of the families Vespertilionidae, Mormoopidae, Molossidae, and Natalidae. The Random forest model (81.3%) was the better species predictor over the artificial neural network and the discriminant function analysis (69% and 62.1%, respectively). Our work is one of the few attempts to do this exercise that has been conducted in Mexico. The library can be useful as a starting point of research in other regions of the highlands in central Mexico where the information is still scarce. [ABSTRACT FROM AUTHOR]