Summary 1.To predict how plant populations may respond to changes in the environment or management, it is necessary to quantify the factors influencing their population dynamics and distributions over large spatial and ⁄ or temporal scales. 2. Most studies of plant population dynamics monitor demography at the sub-metre scale. Extrapolation or prediction from these studies is difficult because the data are sparse, parameter error cannot be ascertained and the data may not cover the range of expected environmental conditions. 3. Here, we describe a survey method based on density-structured models. These models use a discrete density state variable and model rates of transition between density states. Although analytically simple, these models are empirically useful as they may be parameterized using readily collected data. They also offer an empirical link between meso-scale and macro-scale population dynamics. 4. For a large-scale study on annual weeds, we describe the rapid estimation of densities using relatively coarse density estimates using visual estimates of density. Using information from detailed surveys, we describe how we use the method to measure populations of annual plants to a scale of 20 · 20 m in areas of up to 4 ha per population within 500 different arable fields over 3 years. 5. We show that the approach taken is repeatable within and among observers, and we quantify the degree of measurement error. We give examples of the resultant data, and compare these with the data obtained from nested small-scale plots. Finally, we show how the information from this type of survey can be incorporated into population models and used to measure within-population and inter-annual flux.