Given the ongoing environmental degradation from local to global scales, it is fundamental to develop more efficient means of gathering data on species and ecosystems. Local ecological knowledge, in which local communities can consistently provide information on the status of animal species over time, has been shown to be effective. Several studies demonstrate that data gathered using local ecological knowledge (LEK)-based methods are comparable with data obtained from conventional methods (such as line transects and camera traps). Here, we employ a consensus analysis to validate and evaluate the accuracy of interview data on LEK. Additionally, we investigate the influence of social and bioecological variables on enhancing data quality. We interviewed 323 persons in 19 villages in the Western and Central Amazon to determine the level of consensus on the abundance of hunted and non-hunted forest species. These villages varied in size, socio-economic characteristics and in the experience with wildlife of their dwellers. Interviewees estimated the relative abundance of 101 species with a broad spectrum of bioecological characteristics using a four-point Likert scale. High consensus was found for species population abundance in all sampled villages and for 79.6% of interviewees. The village consensus of all species abundance pooled was negatively correlated with village population size. The consensus level was high regardless of the interviewees' hunting experience. Species that are more frequently hunted or are more apparent had greater consensus values; only two species presented a low consensus level, which are rare and solitary species. We show in our study in the Amazon that information gathered by local peoples, Indigenous as well as non-Indigenous, can be useful in understanding the status of animal species found within their environment. The high level of cultural consensus we describe likely arises from knowledge sharing and the strong connection between the persons interviewed and the forest. We suggest that consensus analysis can be used to validate LEK-generated data instead of comparing these types of data with information obtained by conventional methods.