Muitos animais usam gritos de comunicação complexos nos seus comportamentos sociais. Sabemos em algumas espécies quais parâmetros dos gritos estimulam determinados comportamentos, mas não entendemos como o sistema auditivo codifica os gritos. Tampouco entendemos os mecanismos subjacentes à seletividade neural aos gritos. Nossos estudos do mesencéfalo auditivo do morcego Pteronotus parnellii revelaram um mecanismo neural importante para produzir respostas seletivas aos gritos. Neurônios que integram informação proveniente de várias freqüências diversas mostram seletividade a gritos de comunicação. ''Sensibilidade a combinações'' de freqüências pode ser um mecanismo comum para codificar sons complexos, já que é importante também para codificar sinais de ecolocação.

Many animals use complex communication calls in social behaviors. In some species we know the features in the calls that elicit particular behaviors, but we do not understand how the auditory system encodes the calls. Nor do we understand the mechanisms underlying neural selectivity to calls. Our studies of the auditory midbrain of the Moustached Bat Pteronotus parnellii have revealed a neural mechanism important for generating selective responses to calls. Neurons that integrate information across different frequencies show selectivity to communication calls. "Combination sensitivity" may be a common mechanism for encoding complex sounds because it is also important for encoding echolocation signals.

Assessing the composition of an area's bat fauna is typically accomplished by using captures or by monitoring echolocation calls with bat detectors. The two methods may not provide the same data regarding species composition. Mist nets and harp traps may be biased towards sampling low flying species, and bat detectors biased towards detecting high intensity echolocators. A comparison of the bat fauna of Fazenda Intervales, southeastern Brazil, as revealed by mist nets and harp trap captures, checking roosts and by monitoring echolocation calls of flying bats illustrates this point. A total of 17 species of bats was sampled. Fourteen bat species were captured and the echolocation calls of 12 species were recorded, three of them not revealed by mist nets or harp traps. The different sampling methods provided different pictures of the bat fauna. Phyllostomid bats dominated the catches in mist nets, but in the field their echolocation calls were never detected. No single sampling approach provided a complete assessment of the bat fauna in the study area. In general, bats producing low intensity echolocation calls, such as phyllostomids, are more easily assessed by netting, and bats producing high intensity echolocation calls are better surveyed by bat detectors. The results demonstrate that a combined and varied approach to sampling is required for a complete assessment of the bat fauna of an area.

In this study the incidence of moths and beetles was examined from feces samples of bats that use different foraging behaviors. Twenty sites around the Fazenda Intervales, a Field Research Station located in São Paulo State, in southeastern Brazil were sampled. Feces were collected from bats caught in mist nets, Turtle Traps or hand nets and, in one case, from beneath a roost. Feces samples were taken from six species of bats: Micronycteris megalotis (Gray, 1842), Mimon bennettii (Gray, 1838), Furipterus horrens (F. Cuvier, 1828), Myotis riparius Handley, 1960, Myotis ruber (E. Geoffroy, 1806) and Histiotus velalus (I. Geoffroy, 1824). To record and describe the frequencies dominating bat echolocation calls, an Anabat II bat detector coupled with an Anabat ZCA interfaces and DOS laptop computers were used. The data show that Furipterus horrens feeds extensively on moths, as predicted from the features of its echolocation calls. Gleaning bats, whose echolocation calls are much less conspicuous to moths take a wide range of insect (and other) prey.