Involving communities in sustainable wildlife management in tropical forests can ensure food security and livelihoods of millions of forest dwellers that depend on wild meat, and also safeguard hunted species. Mathematical models have been developed to assess hunting sustainability; but these require empirical information on reproductive parameters of the prey species, often challenging to obtain. Here, we suggest that if local people can accurately identify the reproductive status of hunted animals in the field, these data could fill the existing knowledge gap regarding species' life-history traits and enable better assessments of hunting impacts. We first tested whether local people in 15 rural communities in three Amazonian sites could accurately diagnose, before and after training, the pregnancy status of hunted pacas Cuniculus paca, which we use as our model. We then applied the results from these tests to correct reproductive status data of hunted specimens, voluntarily collected over 17 years (2002-2018) as part of a citizen-science project in one of our study sites. We ran generalized additive models to contrast these corrected reproductive rates with those obtained from the direct analysis of genitalia by researchers, and with indices describing game extraction levels (catch-per-unit-effort, CPUE, and age structure of hunted individuals). Before training, interviewees correctly diagnosed pregnancy in 72.5% of tests, but after training, interviewees accurately diagnosed pregnancy in 88.2% of tests, with high improvements especially for earlier pregnancy stages. Monthly pregnancy rates determined by hunters and by researchers were similar. Reported annual pregnancy rates were negatively correlated with CPUE, and positively correlated with the percentage of immatures in the hunted population, in accordance with an expected density-dependent response to variations in hunting levels. Synthesis and applications. We show that the voluntary diagnosis of game species' reproductive status by local people is a feasible method to obtain accurate life-history parameters for hunted tropical species, and to assess hunting effects on game populations. Given that almost half of the protected areas in the world are co-managed by local people, our results confirm the potential of integrating local communities in citizen-science initiatives to ensure faster, low-cost and more accurate data collection for wildlife management.