Harvest data are widely used to understand hunting in tropical forests. However, survey methods are susceptible to biases which could affect results. We compare catch data from two approaches applied concurrently in the same villages (n=7) in Gola Forest, Liberia: hunter recall interviews (n=208 hunters, 253 trips) and continuous monitoring by village-based assistants (n=53 hunters, 404 trips). We use Bayesian multi-level models to: (1) compare estimates of animals killed per trip for each data source; (2) test whether differences between villages are consistent across data sources and (3) identify potential sources of bias. Hunter recall produced higher, and more variable, catch estimates than village-based monitoring, with mean of 7.3 animals [6.0-8.8 95%CI] compared to 3.0 [2.4-3.6], for a trip lasting 3.2 days (the average duration from village-based monitoring). Mean catch-per-village from village-based monitoring failed to predict hunter recall catch and villages with highest catch differed between methods. Differences in trip duration was a potential source of bias: hunter recall recorded longer, more variable, trips (mean 4.0±SD3.0 days, range=1-32) than village-based monitoring (mean 40 3.2±SD1.7, range=1-10). Longer trips were associated with higher catch-per-day, use of guns, forest camps and accompaniment by another person; so non-random sampling of these traits
may have introduced bias. Between-hunter variability was lower with village-based monitoring, suggesting sampling captured a less diverse subgroup of hunters, or that recall data were noisier due to reporting errors. Our results demonstrate that methodological biases can have large effects on catch estimates and should be carefully considered when designing or interpreting hunting studies.