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Worldwide, many large mammals are threatened by poaching. However, understanding the causes of poaching is difficult when both hunter and hunted are elusive. One alternative is to apply regression models to opportunistically-collected data but doing so without accounting for inherent biases may result in misleading conclusions. To demonstrate a straightforward method to account for such biases, and to guide further research on an elusive Vulnerable species, we visualized spatio-temporal poaching patterns in 844 Andean bear Tremarctos ornatus presence reports from the Cordillera de Mérida, Venezuela. To create maps of poaching risk we fitted two logistic regression models to a subset of 287 precisely georeferenced reports, one ignoring and one including spatial autocorrelation. Whereas the variance explained by both models was low, the second had better fit and predictive ability, and indicated that protected status had a significant positive effect on reducing poaching risk. Poaching risk increased at lower altitudes, where all indicators of human disturbance increased, although there was scant evidence that human-bear conflicts are a major direct trigger of poaching events. Because highest-risk areas were different from areas with most bear reports, we speculate that hunting may be driven by opportunistic encounters, rather than by purposeful searches in high-quality bear habitat. Further research comparing risk maps with bear abundance models and data on poaching behaviour will be invaluable for clarifying poaching causes and for identifying management strategies. [ABSTRACT FROM AUTHOR]