Influência da gestão territorial sobre a densidade e condição de Pirarucus (Arapaima gigas (Schinz in Cuvier, 1822) no Estado do Amazonas, Brasil

Abstract

The Pirarucu management was first implemented in 1999 in the Mamirauá Sustainable Development Reserve (RDS Mamirauá), using the counting method, which allows the population census estimation of pirarucus and controlled harvesting. With the expansion of the management model to other areas in the state of Amazonas, the most diverse forms of territorial management emerged. Therefore, this study aimed to evaluate whether the type of territorial management (Fisheries Agreement, Indigenous Land and Conservation Unit) and management time lag influence the density as well as the condition of the fish. For this purpose, the information from the annual reports and the monitoring sheets sent to IBAMA, an institution that regulates the fishing in the state, were used. For the estimation of the population densities, the number of individuals from counting per lake area (ind/ha) was used; the condition was calculated based on the parameters a and b from the length/weight regression. The analysis of the influence of the territorial management type and time was carried out by means of ANOVA, ANCOVA and simple regressions. The results indicated that the management is sustainable and similar in all types of territorial management. The pirarucu has negative allometric growth, with a greater increase in length rather than weight; no density differences were observed within and between the types of management of lakes and the time of management did not influence the density in the various types of territorial management; the estimated density for all management types can be considered high; there was a decrease in the condition of the fish in Indigenous Land, over time. Future research should include environmental features, ecological and ethnic metrics in order to better understand variations in condition and density; use an extended time series of data to increase the chance to detect systematic variations and reliability of the results.