Accuracy, uncertainty, and biases in cumulative pressure mapping

•Our study addresses biases and uncertainties of cumulative pressure maps.•Map’s accuracy significantly improves with an increase in the number of layers.•Uncertainties in intensity scores moderately affect overall accuracy.•Additive and antagonist cumulative models exhibit a robust correlation. Understanding how human activities are altering landscapes is critical to address habitat loss and biodiversity decline. Cumulative pressure mapping has emerged as a tool to quantify both the extent and intensity of multiple forms of human activities on the environment. However, there are several approaches to selecting and combining individual spatial layers into cumulative pressure maps, without clear guidance on how these methods affect the accuracy of the resulting maps. Here, we evaluated how the number of individual pressures, and changes in their intensity scores influenced the accuracy, measured against visual interpretation of high-resolution imagery, of a cumulative pressure map for a large, ecological diverse province, British Columbia, Canada. Additionally, we compared additive and antagonist models for combining pressures, which are among the most widely employed models in terrestrial studies. We started by identifying 16 human pressures and their associated spatial representation (i.e., layer) across the province. We then compared the validation values and the outcomes of 100,000 simulations in which we tested different perturbations of the human pressure model. Model accuracy improved with the inclusion of each additional pressure layer, reaching an average mean absolute error of 0.09 with the full spectrum of pressures. Our findings suggested that variations in intensity scores assigned to individual pressures only moderately influenced the resulting cumulative pressure score. In our final analysis, we observed a robust correlation between the additive and the antagonist models, particularly in regions that were either relatively free of human disturbance or highly modified by disturbances. Our study provides an empirical basis for continued improvements to practices for cumulative pressure mapping, addressing methodological challenges that were not formally considered in previous studies.