Marine Protected Areas (MPAs) are effective resource management and conservation measures, but their success is often hindered by non-compliant activities such as poaching. Understanding the risk factors and spatial patterns of poaching is therefore crucial for efficient law enforcement. Here, we conducted explanatory and predictive modelling of poaching from recreational fishers within no-take zones of Australia’s Great Barrier Reef Marine Park (GBRMP) using Boosted Regression Trees (BRT). Combining patrol effort data, observed distribution of reported incidents, and spatially-explicit environmental and human risk factors, we modeled the occurrence probability of poaching incidents and mapped poaching risk at fine-scale. Our results: (i) show that fishing attractiveness, accessibility and fishing capacity play a major role in shaping the spatial patterns of poaching; (ii) revealed key interactions among these factors as well as tipping points beyond which poaching risk increased or decreased markedly; and (iii) highlight gaps in patrol effort that could be filled for improved resource allocation. The approach developed through this study provide a novel way to quantify the relative influence of multiple interacting factors in shaping poaching risk, and hold promises for replication across a broad range of marine or terrestrial settings.