Plantas medicinais, redundância utilitária e resiliência de sistemas médicos locais na caatinga

Abstract

The Utilitarian Redundancy Model (URM) infers about socioecological systems resilience based on an ecological model. According to this model, the redundancy of species with the same utilitarian function ensures systems resilience. Under this perspective, the study about resilience of local medical systems (LMS) allows one to detect essential matters in order to understand how local communities can hold themselves and evolve in specific environments, especially where the access to natural resources and public health is limited. This dissertation aims to investigate the resilience of two LMS in Caatinga and to test some URM predictions. Therefore, our questions are based in features that give LMS resilience which follow the model's predictions: Do medicinal categories have redundancy prevalence? In the same medicinal category, are the redundant species used in the absence of preferred species? Is redundancy bigger in more severe and/or more frequent illnesses? Is information about illness treatment shared? The results show that the medical systems are highly specialized. However, when there is redundancy, the redundant species are the main strategy used when a disturbance occurs. This confirms an URM assumption in which redundancy ensures the maintenance of therapeutic functions. The redundancy of an illness was founded to be related to its frequency of occurrence, what guarantees the maintenance of medicinal functions in usual events. Despite this fact, the low rates of sharing we have found and the little redundancy in severe illnesses evidence points of vulnerability in the system. Based in our data, we might suggest that the studied LMS are mainly structured by means of specialists individual experiences and that the addition of new plants follows the illness dynamics of occurrence and considers life-threatening in the treatment. Under this perspective, the low sharing and the little redundancy in serious illnesses might reflect a system's evolutionary process feature.