Sigmodontine rats are one of the most diverse components of the Neotropical mammal fauna. They exhibit a wide ecological diversity and a variety of locomotor types that allow them to occupy different environments. To explore the relationship between morphology and locomotor types, we analyzed traits of the postcranial osteology (axial and appendicular skeletons) of 329 specimens belonging to 51 species and 29 genera of sigmodontines exhibiting different locomotor types. In this work, postcranial skeletal characters of these rats are considered in an ecomorphological study for the first time. Statistical analyses showed that of the 34 osteological characters considered, 15 were related to the locomotor types studied, except for ambulatory. However, character mapping showed that climbing and jumping sigmodontines are the only taxa exhibiting clear adaptations in their postcranial osteology, which are highly consistent with the tendencies described in many other mammal taxa. Climbing, digging and swimming rats presented statistically differences in traits associated with their vertebral column and limbs, whereas jumping rats showed modifications associated with all the skeletal regions. Our data suggest that sigmodontine rats retain an all-purpose morphology that allows them to use a variety of habitats. This versatility is particularly important when considering the lack of specialization of sigmodontines for a specific locomotor mode. Another possible interpretation is that our dataset probably did not consider relevant information about these groups and should be increased with other types of characters (e.g. characters from the external morphology, myology, etc.).

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A new approach for biogeography to find patterns of sympatry, based on network analysis, is proposed. Biogeographic analysis focuses basically on sympatry patterns of species. Sympatry is a network (= relational) datum, but it has never been analyzed before using relational tools such as Network Analysis. Our approach to biogeographic analysis consists of two parts: first the sympatry inference and second the network analysis method (NAM). The sympatry inference method was designed to propose sympatry hypothesis, constructing a basal sympatry network based on punctual data, independent of a priori distributional area determination. In this way, two or more species are considered sympatric when there is interpenetration and relative proximity among their records of occurrence. In nature, groups of species presenting within-group sympatry and between-group allopatry constitute natural units (units of co-occurrence). These allopatric units are usually connected by intermediary species. The network analysis method (NAM) that we propose here is based on the identification and removal of intermediary species to segregate units of co-occurrence, using the betweenness measure and the clustering coefficient. The species ranges of the units of co-occurrence obtained are transferred to a map, being considered as candidates to areas of endemism. The new approach was implemented on three different real complex data sets (one of them a classic example previously used in biogeography) resulting in (1) independence of predefined spatial units; (2) definition of co-occurrence patterns from the sympatry network structure, not from species range similarities; (3) higher stability in results despite scale changes; (4) identification of candidates to areas of endemism supported by strictly endemic species; (5) identification of intermediary species with particular biological attributes.